June 30th was a milestone day that we've been waiting a long time for. Yesterday it came and went. June 30th was the last day we were both employed. The day we received our last paychecks. And it was our four year wedding anniversary (surprisingly Mike was the first to remember. We've been so caught up with preparing for our trip that it almost went unnoticed).
With June 30th passing, we open the next chapter in our lives. No more alarm clocks, no more commute to work, no more income. As an exciting new feature we've reversed the direction of our travel funds progress bar and it will now begin its (hopefully) slow descent towards 0%. For those that are relatively new to this blog, we've long said that our trip will end when we get tired of traveling or we run out of money.
This post is a little different than past posts in that we decided to team write it. Not exactly collaboratively, but independently one after the other.
Yesterday was my last day of work. It kind of feels surreal. I can no longer introduce myself as a high school math teacher (at least, not for the next little while), but I will always be a teacher at heart.
I think the moment it really hit me was when I selected all of my files on my work laptop and hit the delete key. I kind of thought that deleting over four years worth of work would be more epic and time-consuming, but ten seconds later everything was gone. Symbolically, deleting all of my notes, assignments, and tests meant saying goodbye to my four years of teaching at that school. To all my teacher friends: I'm not totally crazy… I do have all my work saved on a flash drive which will be backed up before I go).
Handing over my keys and laptop to our school secretary, Lorrie. Photo credited to Mr. Joshua Bekker.
I have to admit, I cried a couple of times when I had to say goodbye to some of my colleagues that I have worked closely with and who have become great friends. Thanks for four great years guys!
Over the past couple of years I have been working with teams of teachers to do innovative things with Grade 9 classes. During this process, we were often told to "go slow." And now, that's exactly what I'm about to do. "Go slow" is our new traveling mantra. There's so much to see and experience in this world… there's no reason to rush through it all.
I'm not going to get all emotional and blubbery, it's just not my style. Yesterday I left the company that I've worked for since my last year of university 7 years ago. One thing I intended to do, but didn't get around to on my last day, was to send out an email to my co-workers thanking them for being so great to work with and to give them the address of this blog. If you happen to be one of the lucky few that I managed to give a Traveled Earth Buisness Card to, please feel free to pass on my thanks and this blog's address to any co-workers that you think may be interested.
Work was kind enough to throw me a bit of a sendoff party at my favorite Regina establishment: Bushwakkers. I'd like to thank everyone that made it out, I had a lot of fun. For those that couldn't make it, no worries, I received your well wishes by email, phone, and word of mouth and appreciated them all. So, one last time – goodbye, thanks for making the last seven years so great, take care.
Congratulations on your new freedom and poverty. I bet it is feeling so real now. Good luck for Wednesday – you'll have to wait for the bus driver to stop before you pee now.
Thanks Shane, things are feeling very real now. At least airplanes have working bathrooms on board.
Hi Mike and Ashley, I'm Shannon. Congrats on embarking on your new lifestyle! I came across your site looking for others who are doing RTW trips in 2011. I gave my notice to work last week and I leave for my RTW July 30th. Yay!! Looks like we have the same approach to traveling-finding work along the way and go as long as possible. You guys have a lot of great information here. I look forward to following you!
Hi Shannon! Thanks for checking out our blog! When we first came up with the idea to travel long term, we were amazed by how many other people are out there doing the same thing. The next month is going to fly by… trust me! Good luck with all your final preparations!
Thanks for the photo cred. I'm on my way to fame! If you're ever in Japan, let us know – Sarah's parents would probably put you up.
Josh – Before you know it, you're going to be earning millions in the iPhone photo market. It takes a special touch to get a great photo. Don't forget to email us a list of your favourite Guatemala locations… we may be there sooner than expected!
Hey, congratulations!! I've managed to establish a pretty good history of unemployment and must say, I really enjoy my time off. I share the same sentiment – when people ask me how long I'll be here (currently Peru) my response is usually "until the money runs out".
Thanks Will, it's the most honest and accurate of an answer we've been able to come up with.
Don't wory Carrie, if you want to go on your own RTW, you will. It's been a lot of hard work for us to get here. We too went through racking up student loans and eventually paying them off (last one only a couple of months ago). Thing is, once you have a goal to work towards, it's amazing how fast you can get there. I only wish we had figured out what we wanted to do years prior.
Congrats, that's a HUGE first step!
All of us here at the pit wish you both the best of luck and hope that you enjoy your trip it sounds very exciting!!I had a blast getting to you know you both better Thursday night at the "Farewell Bash"! I would also like to Congratulate you guys on taking this exciting and courageous journey!
Again we all wish the both of you the best of luck and look forward to following your blog and reading about all of your new experiences and where this journey will take you!
Good Luck on your flight tomorrow!!
I would like to wish you farewell and the best of luck in everything!
Thank you Stephanie, it was also nice getting to know you a bit better Thursday. Give our regards to everyone at the pit.
Well, it's a good thing I was not at school that day, I would have made your departure worse……I would have cried, it is truly sad to see you go, you will be missed, and I will miss our lunches we had together. All the Best!
Thanks Pam! I'll miss our lunches and conversations too. I have definitely shed more than a few tears from all the goodbyes over the last week.
You mentioned a travel funds progress bar…I bet it's super obvious but I am not seeing it?
Cheers and if you ever get near the Columbia River Gorge you are welcome to stay with us..we live in the woods right across from the high school I teach at!
The progress bar has been and gone… Once we finished saving up and started spending we decided to remove it. It's fun to watch a progress bar go up. Watching it fall is a little less exciting. At least for us.


text:
Q: My keyboard event isn't firing and I don't know why I'm adding an event listener to my paragraph element in Javascript. My paragraphs are inside my 'main' HTML element. when the user presses the keyboard button 'AltGr' (key code 18) the paragraph is supposed to become a child of its predecessor. But the event itself isn't firing. Why is this and how do I solve the problem?
let m=document.getElementById('textEdit');

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The event gets attached whenever p is generated.

redpajama_set_name: RedPajamaStackExchange


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Arnaldo Rosa Prata, conhecido como Rosa Prata (Uberaba, 10 de abril de 1927) é um político brasileiro, agropecuarista, professor e também empresário. Foi prefeito de Uberaba pelo partido Aliança Renovadora Nacional (Arena) entre 1971 e 1973. E foi Deputado Federal Constituinte de 1987 a 1991, pelo PMDB. É filho do fazendeiro João Prata Júnior e de Délia Rosa Prata. Seu primo Alaor Prata e tio-avô Fidélis Reis vêm de uma carreira política, respectivamente, prefeito da cidade do Rio de Janeiro de 1922 a 1926, e deputado federal entre 1921 a 1934.

Formação
Concluiu, em 1946, os estudos secundários no Colégio Andrews, no Rio de Janeiro, então Distrito Federal. Formou-se em Agronomia, em 1950, pela Universidade Rural do Brasil, atual UFRRJ.

Vida e carreira
Filho do fazendeiro João Prata Júnior e de Délia Rosa Prata, Arnaldo Rosa Prata nasceu em Uberaba, Minas Gerais, no dia 10 de abril de 1927. Se formou agrônomo em 1950 pela Universidade Rural do Brasil, no Rio de Janeiro, atual Universidade Federal Rural do Rio de Janeiro (UFRRJ).

Ingressou na vida política em 1971, aos 44 anos, elegendo-se prefeito de Uberaba pela legenda da Aliança Renovadora Nacional (Arena). Foi eleito em novembro de 1970 e assumiu o cargo em 31 de janeiro de 1971, o qual deixou dois anos depois. Após o mandato, foi fundador e secretário-geral da Confederação Mundial de Criadores de Zebu, representando a Associação Brasileira de Criadores de Zebu, até 1982. Nesse meio tempo, foi também diretor da Faculdade Zootécnica de Uberaba (1978-1982) e Secretário de Agricultura de Minas Gerais (1983-1986).

Com o fim do bipartidarismo em novembro de 1979, Rosa Prata filiou-se ao Partido Popular (PP), liderado por Tancredo Neves e acabou incorporado ao Partido do Movimento Democrático Brasileiro (PMDB) em 1982. Neste ano, já como peemedebista, concorreu pela segunda vez à prefeitura de Uberaba mas foi derrotado por Vagner do Nascimento. No ano seguinte, em 1983, foi nomeado secretário de Agricultura de Minas Gerais no governo de Tancredo Neves (1983-1984), cargo em que permaneceu até 1986, no governo de Hélio Garcia.

Em novembro de 1986, se elegeu deputado federal constituinte por Minas Gerais na legenda do PMDB, apoiado pela União Democrática Ruralista (UDR), e tomou posse em 01 de fevereiro de 1987, ano em que teve início a Assembleia Nacional Constituinte (ANC). No mesmo ano, Rosa Prata foi suplente na Subcomissão da União, Distrito Federal e Territórios, da Comissão da Organização do Estado, e titular da Subcomissão da Política Agrícola e Fundiária e da Reforma Agrária, da Comissão da Ordem Econômica.

Rosa Prata tentou a reeleição em 1990, pela legenda do Partido das Reformas Sociais (PRS), mas não teve êxito. Deixou, assim, a Câmara dos Deputados em janeiro do ano seguinte, assim como a carreira política como um todo. Desde então, dedicou-se apenas a atividades empresariais no setor agropecuário, em Uberaba.

Carreira
1951 -  Torna-se agrônomo responsável pelo Departamento de Máquinas Agrícolas da Companhia Comercial Brasileira de São Paulo

1952 - Torna-se administrador-geral e responsável técnico pelas atividades agropecuárias das fazendas Capivara e Volta Grande

1956 a 1960 - Preside a Sociedade de Agrônomos e Veterinários de Uberaba

1962  a 1966 - Assume a Secretaria Geral da Sociedade Rural do Triângulo Mineiro

1968 a 1970 - Torna-se Presidente Associação Brasileira de Criadores de Zebu (ABCZ), em Uberaba

1970 a 1973 - Elege-se Prefeito de Uberaba pela Aliança Renovadora Nacional (ARENA)

1974 a 1978 - Volta à presidência da ABCZ, onde também foi membro dos conselhos diretivo e fiscal.

1976 - Funda a Confederação Mundial de Criadores de Zebu, onde ocupou o cargo de secretário-geral até 1982

1979 - Filia-se à agremiação liderada por Tancredo Neves, o Partido Popular (PP). E, em 1982, o partido incorporou-se ao PMDB

1982 - Concorre ao cargo de prefeito de Uberaba, mas perde para  Vagner do Nascimento

1983 a 1986 - É nomeado secretário de Agricultura de Minas Gerais

1984 - Viaja, oficialmente, para representar o governo do estado em estudos dos problemas de irrigação, produção agrícola, abastecimento e comercialização de gêneros alimentícios e alternativas energéticas

1986 a 1990 - Elege-se deputado federal constituinte por Minas Gerais, pelo PMDB

1987 - Participa como suplente da Subcomissão da União, Distrito Federal e Territórios, da Comissão da Organização do Estado, e, como titular, da Subcomissão da Política Agrícola e Fundiária e da Reforma Agrária, da Comissão da Ordem Econômica.

1990 - Candidata-se à reeleição como deputado federal,  pelo Partido das Reformas Sociais (PRS). Mas não obtém vitória

Deputados federais do Brasil por Minas Gerais
Deputados federais da Assembleia Nacional Constituinte de 1987–1988
Naturais de Uberaba

redpajama_set_name: RedPajamaWikipedia


Cromos Pharma's CEO Dr. Vlad Bogin has published an article in the leading Russian Pharma periodical "Pharmvestnik". The article discusses the possible reasons for discrepancies in the results of the TOPCAT trial.
Please follow the link to the article that provides the detailed insight into this issue.


Hello there, I am Daisy, I am a very friendly and fun person, I would love to be your English instructor here at native camp. I know that learning a different language is not easy, that is why I am here. I am ready and willing to share to you the knowledge that I have with the English Language,Lets make learning English fun. We can discuss anything under the sun. What are you waiting for ? Book my class now and let's start learning English together, See you in my class.
Teacher Daisy got her Bachelor's Degree in Nursing last 2005. She enjoys talking to other people and learning different culture, During her freetime, she would watch English comedy, drama or love story movies.
Thank you for the class. It was clear and easy understand.
I enjoyed your lesson so much! Thank you!!


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zauweasel
"I was allowed to wail and keen, as the Irish call it, on a nightly basis. Most of the problem of grief is that people bottle it up. But I could use these songs to keep myself sane. I was having conversations in these songs that maybe I couldn't have with people." - Bono on the period when his father was dying while they were on the Elevation tour
The album Songs of Innocence used to start with 'This Is Where You Can Reach Me,' which was always supposed to be the first song. U2 then changed this to Raised by Wolves, but ultimately decided to "put the songs first", just like on Joshua Tree.

redpajama_set_name: RedPajamaCommonCrawl


First Nations used a tool called the "atlatl", a specially fashioned piece of wood used to obtain more force when throwing a spear. The atlatl was the first known device made by humans to operate and improve the performance of another tool.
Columbus made four voyages to the new world, the first time in 1492, which took him to the Caribbean Islands; a second took him to Cuba; a third to the northeast coast of South America and a fourth took him to Central America. He never did set foot on the land that he is given credit for discovering.
Before Europeans arrived 500 years ago, approximately 60% of First Nations were involved in farming as a way of life. Foods introduced by First Nations people now make up a significant part of the world's food production. Approximately 50% of the value in today's world food production comes from original North American plants. Near the top of the list are corn and potatoes, the second and third most popular foods in the world.


There are many ways to make the cost of concurrency lower, but few met the team's needs. To go 10X faster, concurrency costs needed to be all but eliminated. Since there's only one way to do that, the choice was obvious.
All data operations in VoltDB are single-threaded, running each operation to completion before starting the next. Fancy concurrent B-Trees and lockless data structures are replaced by simple data structures with no thread safety or concurrent access costs. Not only does this meet speed goals, it's actually much simpler to build, test and modify, a double-win.
Note that this choice is only really possible with a memory-centric design. The latency of reading and writing from disk is often hidden by shared memory multi-threading, but when running single-threaded, that latency will cause the CPU to spend most of its time idle. Eliminating disk waits allowed us to keep the CPU saturated, and run rings around traditional architectures.
As an operational store, the VoltDB "operations" in question are actually full ACID transactions, with multiple rounds of reads, writes and conditional logic. If the system is going to run transactions to completion, one after another, disk latency isn't the only stall that must be eliminated; it is also necessary to eliminate waiting on the user mid-transaction.
That means external transaction control is out – no stopping a transaction in the middle to make a network round-trip to the client for the next action. The team made a decision to move logic server-side and use stored procedures.
There are some drawbacks here. External transaction control can make certain problems easier. Splitting business logic between the client and server requires additional organization. Also, most object-relational-mappers (ORMs) rely on external transaction control.
There are no perfect answers to these questions. The good news is these problems apply to any system designed to scale. One of the common ways to make any RDBMS faster is to reduce contention by eliminating external transaction control. At scale, the system shouldn't check in with the client over a network while it's holding locks. And while there are many criticisms of stored procedures, VoltDB addresses many of them. Rather than product-specific SQL-derived languages, VoltDB uses Java as its procedure language. Java is fast, familiar, and has a huge ecosystem. It's even possible to step through stored procedure code in an Eclipse IDE.
Much has been written about ORMs, which are famous for impeding scale. They make it easy to build large complex applications that work for low to moderate throughput, but quickly become the point of contention at scale.
At a fundamental level, stored procedures move code to the data, and external transaction control moves data to the code. One of these things is much bigger than the other, so that's why VoltDB was designed to move code, not data.
The team building VoltDB solved the concurrency problem by doing one thing at a time using a single-threaded pipeline. But how does that reconcile with multi-core hardware?
First, the decision was made to have VoltDB scale to multiple machines. The team needed to figure out a way to manage multiple single-threaded pipelines across a cluster. To deal with multi-core, why not simply move the abstraction? Treat four machines with four cores each as sixteen machines. Each core gets a single-threaded pipeline, and build the system to manage them in aggregate. The database thus can scale to lots of cores, lots of nodes or both. All the while, there are no concurrent data structures, locks or other expensive synchronization in the data management layer.
The next goal was to keep the pipelines full. VoltDB does this by partitioning the data. Say a finance app is partitioned by ticker symbol; now all operations on "MSFT" can be directed toward the appropriate pipeline. If an app is partitioned by customer, an operation on a specific customer's data can be routed easily to the right pipeline. It's also possible to operate on data across pipelines by breaking the work up into per-pipeline chunks and processing per-pipeline results into global results. For more details on how transactions work in a partitioned environment.
VoltDB calls this approach "concurrency through scheduling." There are still threads and locks in the system, mostly to support networking and scheduling transactions, but all SQL and Java procedure logic is single-threaded and blindingly fast. The locks are there to schedule things that are low-contention; they protect small scheduling data structures, rather than user data for a transaction.


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Tigers and Leopards Safari Tours | Kenya Safari Animals
Safari Overview
This is an extensive and beautiful journey across Kenya. You'll travel in a 4x4 vehicle to many of the main parks, culminating in the rolling plains of the Maasai Mara. As well as wildlife viewing, insightful excursions range from a full day discovering Nairobi's attractions – including the Daphne Sheldrick Elephant Orphanage – to visiting a local school and lion-proof boma supported by Born Free.
Itinerary (summary)
Activities, Hotel & Meals
On arrival, transfer to your modern hotel to relax
Dinner and overnight at Radisson Blu or similar
Full day trip to four of Nairobi's key attractions
Visit the Giraffe Centre and Daphne Sheldrick Elephant Orphanage
Have lunch at Tamarind Tamambo
Visit the Karen Blixen Museum
Dinner and overnight at the Radisson Blu
Nairobi - Amboseli National Park
Drive to Amboseli National Park
Known for its huge elephant herds and the Mount Kilimanjaro
Arrive at the Amboseli Serena Safari Lodge in time for lunch
Head out on a game drive in the afternoon
Dinner and overnight at the Amboseli Serena Safari Lodge
Visit Ol Moti School, supported by the Born Free Foundation
Visit the Maasai bomas
Drive back to your lodge for lunch
Head out on a game drive in Amboseli
Amboseli - Aberdares National Park
After breakfast travel back to Nairobi
Head out towards the Aberdare Mountain Range
Lunch en-route at the Safari Park Hotel
Check-in at the Aberdare Country Club
Transfer to its unique sister property, The Ark
Aberdares - Samburu National Reserve
Travel back to the Aberdare Country Club for check-out
Continue north to the Samburu National Reserve
Dinner and overnight at the Samburu Game Lodge on full board basis
Make the most of a full day in Samburu
Early morning and afternoon game drives
Meals and overnight at Samburu Game Lodge on a full board basis
Samburu - Ol Pejeta
Drive south to Sweetwaters Serena Camp for lunch and a game drive
Visit the Chimpanzee and Rhino Sanctuaries
Meals and accommodation at Sweetwaters Serena Camp on a full board basis.
Today offers the opportunity for further game drives in the conservancy.
Ol Pejeta - Lake Nakuru National Park
Drive via Thomson's Falls to Lake Nakuru
Stay at the Sarova Lion Hill Lodge overlooking Lake Nakuru on full board basis
Lake Nakuru - Maasai Mara Game Reserve
Drive to the Maasai Mara Game Reserve
Stay at the Sarova Mara Game Camp on full board basis
Early morning, mid-morning and afternoon game drives in the Maasai Mara
Option: take a hot-air balloon safari over the Mara
Maasai Mara - Nairobi
Fly directly from the Mara to Mombasa in the afternoon or return to Nairobi
Spend the afternoon at the Eka Hotel (Nairobi)
(End of tour).
Day 1: Nairobi
On arrival, transfer to your modern hotel to relax.
Dinner and overnight at Radisson Blu or similar.
Enjoy a leisurely breakfast before an insightful full-day trip to four of Nairobi's key attractions. Your first stop will be the Giraffe Centre, a conservation center for the endangered Rothschild's giraffe where you'll have the chance to feed these friendly giants. Next you'll drive to the world-famous Daphne Sheldrick Elephant Orphanage. Spend some time with the elephants as they are fed by staff, who are on hand to answer any questions and talk about their experiences with these rescued baby elephants. The elephants are inquisitive, so expect a trunk heading in your direction! Have lunch at Tamarind Tamambo, and then visit the Karen Blixen Museum; the simple farmhouse home of Karen Blixen, author of Out of Africa.
Your final visit of the day will be Kazuri Beads – a bead-making shop founded in 1975 to create employment for local single mothers. You'll see how the beads are hand-shaped from clay, kiln-fired, and glazed before being strung together to create a colorful bracelet or necklace. Dinner tonight is at your own arrangement.
Dinner and overnight at Radisson Blu or similar on a bed only basis.
Day 3: Nairobi - Amboseli National Park
After breakfast, drive to one of the most popular parks in Kenya – Amboseli National Park, known for its huge elephant herds and dramatic backdrop of Mount Kilimanjaro. You'll arrive at the Amboseli Serena Safari Lodge in time for lunch. This a traditional lodge, where rooms are housed in single-story buildings. In the afternoon, head out on a game drive. Amboseli's low vegetation makes for excellent wildlife spotting. Look out for lion, buffalo, and zebra, as well as a hippo in the swamplands. The park's dry and dusty form is due to the volcanic ash that came from Mount Kilimanjaro on its last eruption, yet the lush green areas are a result of a continuous water supply from the mountain's melting snow. Approx. journey time: 3 hours.
Accommodation at Amboseli Serena Safari Lodge or similar on a full board basis.
This morning you'll visit Ol Moti School, supported by the Born Free Foundation (you may like to bring small gifts for the children such as pens, books, and posters). Then visit one of the lion-proof Maasai bomas created by Born Free in collaboration with the Kenya Wildlife Service. Amboseli is a region of high human-wildlife conflict, with lions destroying livestock, and the local community destroying the lions in return. Typically, Kenyan livestock is held in enclosures made of thorn bushes − which lions can find a way through. The difference with a lion-proof boma is that it is constructed of chain fences. So far, this method – piloted in Amboseli – has proved to be 100% lion proof. Drive back to your lodge for lunch and head out on a game drive in Amboseli.
Day 5: Amboseli - Aberdares National Park
After breakfast travel back to Nairobi and then out towards the Aberdare Mountain Range, with lunch en-route at the Safari Park Hotel. Once you reach Aberdare you'll check-in at the Aberdare Country Club in order to transfer to its unique sister property, The Ark, which will be your viewing perch for the night. Based on Noah's Ark, The Ark overlooks a floodlit waterhole and salt lick, attracting wildlife both day and night. Approx. journey time: 5 hours.
All meals and accommodation at The Ark.
Day 6: Aberdares - Samburu National Reserve
Travel back to the Aberdare Country Club for check-out and continue north to the Samburu National Reserve and the Samburu Game Lodge on the western banks of the Uaso Nyiro River. This is the dry north of Kenya and the reserve attracts wildlife not often seen elsewhere. Look out for the gerenuk – a rare gazelle with a long neck that stands on its hind legs to feed. Approx. journey time: 3 hours.
Dinner and overnight at Samburu Game Lodge on a full board basis.
Day 7: Samburu National Reserve
Make the most of a full day in Samburu with early morning and afternoon game drives offering further chances to seek out the 'special five': the gerenuk, Beisa oryx, reticulated giraffe, Grevy's zebra, and Somali ostrich.
Dinner and overnight at Sarova Mara Game Camp.
Meals and overnight at Samburu Game Lodge on a full board basis.
Day 8: Samburu - Ol Pejeta Conservancy
Drive south to Sweetwaters Serena Camp for lunch and a game drive, then visit the Chimpanzee and Rhino Sanctuaries. Within the Ol Pejeta Conservancy, the sanctuary is the only place in Kenya where the endangered chimpanzee can be seen. It also houses the world's two remaining northern white rhino. Head back to Sweetwaters for dinner and to experience the charm of under-canvas living. Approx. journey time: 3 hours.
Meals and overnight at Sweetwaters Serena Camp on a full board basis.
Day 9: Ol Pejeta Conservancy
Today offers the opportunity for further game drives in the Ol Pejeta conservancy.
Day 10: Ol Pejeta - Lake Nakuru National Park
Drive via Thomson's Falls to Lake Nakuru for lunch at Sarova Lion Hill Game Lodge. Lake Nakuru National Park offers one of the best chances to see a rhino. Enjoy a game drive and take in the views from the park's viewpoints. Stay at the Sarova Lion Hill Lodge overlooking Lake Nakuru. Approx. journey time: 3 hours.
Meals and overnight at Sarova Lion Hill Lodge on a full board basis.
Day 11: Lake Nakuru - Maasai Mara Game Reserve
Today there's the option of a hot-air balloon safari over the Mara at sunrise. Then head out on the early morning, mid-morning, and afternoon game drives in the Maasai Mara.
Meals and overnight at Sarova Mara Game Camp on a full board basis.
Day 12: Maasai Mara Game Reserve
Drive to the Maasai Mara and explore the rolling plains on an afternoon game drive. You're likely to spot a pride of lion basking in the shade, and may even be able to seek out an elusive solo leopard and other species including cheetah and hyena. Stay at the Sarova Mara Game Camp – a classic bush camp with traditionally-furnished canvas tents housed under tiled roofs. Approx. journey time: 5 hours.
Day 13: Maasai Mara - Nairobi
If you're traveling on to Kenya's coast, you'll fly directly from the Mara to Mombasa in the afternoon. Otherwise, return to Nairobi. Approx. journey time: 5 hours. Spend the afternoon at the Eka Hotel where you might like to pre-book a day-room to freshen up before your return flight.
Included in tour:
Accommodation as per the above safari description
Meals as specified on the itinerary above
All transfers and transport, park fees, hotel accommodation
Game viewing activities as stated in the above tour/safari description
Service of an English speaking driver guide
Not included in tour:
Extras at the lodges i.e. drinks, telephone, laundry etc.
Visa entry into Kenya or Tanzania
Tips and gratitudes to safari staff
Accommodation for this Safari
Amboseli Serena Safari Lodge
Set against the magnificent backdrop of Africa's highest mountain, the glittering Mount Kilimanjaro, Amboseli Serena Safari Lodge enjoys a uniquely privileged position at the heart of this world-famous national park.
The Ark Lodge
Set in the heart of the Aberdare National Park, the Ark overlooks a floodlit waterhole and salt lick, which attracts a host of awesome wildlife.
Samburu Sopa Lodge
Located inside the Samburu National Reserve in Northern Kenya, Samburu Sopa lodge is built atop high ground allowing panaromic views of the vastness of Samburu shrub land and the hills beyond.
Sarova Lion Hill Lodge
Sarova Lion Hill Game Lodge overlooks the spectacular flamingo pink rimmed Lake Nakuru. Its location affords panoramic views of the lake and the picturesque Lake Nakuru National Park.
Sweetwaters Serena Camp
Sweetwaters Serena Camp provides and amazing wildlife viewing experience. You can see animals from the comfort of your rooms. Located on the plains below Mt Kenya, in the expansive Ol Pejeta Conservancy.
Sarova Mara Game Camp
Sarova Mara Game Camp is located within in the heart of the Maasai Mara Game Reserve, surrounded by 2 streams the camp also features a exotic trees, a large fish pond and an Maasai village adjoining the camp.
Inquiry about this safari
A luxury lodge at Amboseli National Park
Amboseli National Park is located in the south of Kenya, on the Tanzanian border, and is famous for its spectacular view of Mount Kilimanjaro. Amboseli has fantastic game viewing and is particularly well-known for its elephant population.
Kenya safari tours
Over 600 bird species have been recorded too in the park, with 40 species being prey birds, it is a paradise for bird lovers and watchers. The Observation Hill towers accessible in the day, the hilltop offers countless photographic views and an amazing sunset to behold.
Amboseli National Park is towered by Mt Kilimanjaro which stands 5896 meters high and is one of the earliest Kenya's game sanctuaries. It is possible to climb Mt Kilimanjaro or access Tsavo National Parks if staying in the Amboseli.
Game drives, picnics, and walking safaris.
The park offers a wildly different Kenya safari tours game drive and accommodation experience.
Amboseli can be visited at any time of year it is recommended to go in the dry months, which are from June to October and January to February. The short rains peak in November and the long rains in April and May – these months are not recommended for wildlife viewing.
The park is located 365km/226mi southeast of Nairobi. Self-drive from Nairobi to the reserve is a good option. You can also drive from other parks if you're doing a road trip around the country. Taking one of the daily scheduled flights from Nairobi to Amboseli's airstrip is another option.
Stunning view of Mt. Kenya from Aberdares National Park
Aberdare National Park is one of the major national parks in Kenya that offer exceptional hiking terrains and stunning views, the park is located between Mount Kenya to the north and the Great Rift Valley.
A black rhino at the Ol Pejeta Conservancy
The Ol Pejeta Conservancy is an important wildlife conservancy in the Laikipia District of Kenya and the largest sanctuary for black rhinos in East Africa. The Conservancy boasts an astounding variety of animals, including the "Big Five" (the endangered black and white rhino, leopard, elephant, buffalo and lion), Grevy's zebra, Jackson's hartebeest, cheetah and chimpanzee.
Samburu Game Reserve
Giraffes in Samburu National Reserve
Reticulated Giraffe at Samburu National Reserve
The reserve is rich in wildlife with fame for an abundance of rare northern specialist species such as the Grevy Zebra, Somali Ostrich, Reticulated Giraffe, Gerenuk, and the Beisa Oryx. The reserve is also popular with a minimum of 900 elephants. Large predators such as the Lion, Leopard, and Cheetah are an important attraction (Kamunyak the Miracle Lioness that adapted the baby Oryx is a resident in the reserve).
Flamingoes at Lake Nakuru National Park
The Lake Nakuru National Park is one of the top tourist destinations in Africa, famed for the Flamingos that flock to its shores to feed on the Cyanophyte Spirulina Platensis that live in its alkaline waters. It is a very important breeding ground and stop over for many species of birds, and has been designated a UNESCO World Heritage Site.
The wildebeest migration at the Masai Mara Game Reserve
Masai Mara National Reserve is considered a "must see" for any visitor traveling on safari in Kenya. It is on the "to do" list of most Kenyan visitors because the park is graced by a wide variety of unique and exquisite wild life and birds.Masai Mara is the best place to see all of the "Big Five" animals (elephants, lions, leopards, rhinos and buffalo) together. The famous wildebeest migration also takes place at the Mara.


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Canada to admit vaccinated U.S. visitors starting Aug. 9
Inoculated visitors from countries other than the United States will be permitted to enter beginning on Sept. 7.
David Ljunggren and Steve Scherer
Jul 19, 2021 • Last Updated July 19, 2021 • 2 minute read
The Canadian border crossing is seen in Lacolle in this file photo. Photo by Ryan Remiorz /THE CANADIAN PRESS
OTTAWA — Canada on Monday said it would start allowing fully vaccinated U.S. visitors into the country on Aug. 9 for non-essential travel, relaxing a 16-month ban that businesses complained was crippling them.
Inoculated visitors from countries other than the United States will be permitted to enter beginning on Sept. 7. The relaxation depends on Canada's COVID-19 epidemiology remaining favorable, officials said.
"With rising vaccination rates and fewer cases in Canada, we can begin to safely ease border measures," Health Minister Patty Hajdu told reporters.
Businesses in both nations, particularly the travel and airline industries, had demanded an end to restrictions on non-essential travel between Canada and the United States which were imposed in March 2020.
"This will be good for our economy … we welcome the direction and are counting on successful implementation," said Goldy Hyder, president of the Business Council of Canada.
The United States and Canada have until now extended the restrictions every month.
Canadian Public Safety Minister Bill Blair said U.S. officials told him it was likely Washington would extend the U.S. ban for a month when it expires on July 22.
White House Press Secretary Jen Psaki declined to say whether the United States would follow Canada's lead.
"We are continuing to review our travel decisions and we'll be guided by our public health and medical experts … I wouldn't look at it through a reciprocal intention," she told a briefing when asked about Canada's move.
People eligible to enter Canada must have been fully vaccinated at least 14 days beforehand. From Aug. 9, Ottawa is also lifting the requirement that all travelers arriving by air must spend three nights in a hotel.
The government repeated that Canadians should avoid non-essential travel abroad.
The news should be a boost for Canada's hard-hit airlines which have recovered more slowly from the pandemic than their U.S. counterparts.
Shares in Air Canada – the nation's largest airline – were trading down 3% in Toronto on Monday amid a broader decline.
Canada's second-largest carrier, WestJet Airlines, said it was operating at 60% capacity in July compared with pre-pandemic levels in 2019. (Additional reporting by David Shepardson in Washington and Allison Lampert in Montreal; Editing by Paul Simao and Cynthia Osterman)
View on Montreal Gazette
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Chiropractic is a healthcare discipline that emphasizes the power of the body to heal itself without the use of drugs or surgery. Throughout the course of your pregnancy, there will be many physiological and hormonal changes as your body prepares for the birth of your baby.
Our talk aims to offer tips and advice on ways to better manage these changes as your pregnancy progresses.
Chiropractic manipulation is safe for the pregnant woman and her baby and best of all it is completely natural – no medications are needed.


If there's one thing you never have to fear playing online casino games it's a lack of excitement.
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Keep in mind that only bets to the value of £10 or more meeting the requirements will get you a ticket into the Cyber Monday Raffle. During this promotional period no Bet Behinds are included.
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The Cyber Monday Raffle will take place on 26 November during which 25 lucky players will win a superb prize. The prizes range from bonus spins to play at the casino to a glorious VIP shopping experience for two. 25 players will be drawn at random during the raffle of which the players drawn 21st to 25th will each receive 50 bonus spin on the Reel Rush slot.
Players drawn 4th to 20th will each receive a cash prize ranging from £50 to £250 depending on what number they've been drawn at.
The 2nd and 3rd place winners will each receive a GiftnGo voucher, the 3rd place winner will receive a voucher valued at £500 and the 2nd place winner will receive a voucher valued at £1,000.
If the casino cannot reach the winner within 3 days the prize becomes invalid.
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Please support us today with your tax-deductible gift!
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Colorado Springs is the perfect Denver Day trip, weekend getaway, or vacation location. There is always something fun to do in COS and these tips will help you plan the perfect getaway!
If your goal is to immerse yourself in nature you can RV at Garden of the Gods RV Resort, or camp at Golden Eagle Campground or Cheyenne Mountain State Park. Also, consider renting a cabin or cottage. Renting an Airbnb is a perfect solution to find everything from low-budget to luxury accommodations available in the Cheyenne Cañon area. When it comes to hotels you have a wide variety to choose from including our top picks: The Broadmoor, The Cheyenne Mountain Resort and The Hotel Eleganté.
It doesn't matter what time of year you visit our beautiful city as there are activities perfect for every age and interest. This includes several free attractions such as hiking at Pikes Peak, Red Rocks Open Space or Garden of the Gods, spending the day outdoors at North Cheyenne Cañon Park, or visiting the Penrose Heritage Museum, Colorado Springs Pioneers Museum, Olympic Training Center, or the United State Air Force Academy.
If are looking for a new way to explore Cheyenne Cañon, we suggest a guided Segway tour with Cheyenne Cañon Segways. Our tours are perfect for first-time visitors, repeat visitors, and locals. Not only do you get to learn how to ride an off-road Segway, but you visit several unique and beautiful attractions along the way. We have 3 different tours to choose from and can create custom tours for families, corporate outings and large groups. Training is included and you only have to be 12 years old to ride.
Here's to a fun-filled trip to Colorado Springs!


Mike, it's our pleasure! Keep the poems and insights coming. We appreciate you!
Thank you so much Michelle for giving me a place to share these ideas and experiences, I really do appreciate everything you do for Swirl couples, and I love this site endlessly! Thank you once again!


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text:
Las Vegas Springs Preserve consists of  dedicated to nature walks and displays and is owned and operated by the Las Vegas Valley Water District. The Preserve is located approximately three miles west of downtown Las Vegas, Nevada. The Preserve is built around the original water source for Las Vegas, the Las Vegas Springs.

The Springs Preserve includes colorful desert botanical gardens, museum galleries, outdoor concert and event venues, an indoor theater, historic photo gallery and a series of walking trails that meander through a wetland habitat.

The Gardens at the Las Vegas Springs Preserve, previously known as the Desert Demonstration Gardens opened in 1980 at another location.  The gardens now occupy  within the Springs Preserve site.

History
While construction on the preserve began in 2005, the custom designed sound wall separating the site from U.S. Route 95 had been erected earlier.  There also was work over the years to maintain and restore the springs, the waterworks and infrastructure of the springs.  The Preserve opened on June 8, 2007.

Green building efforts

The Preserve shows people how to live in the desert environment and how to take advantage of what is available.  Part of this project showcases a dual use concept.  For example, the parking area is actually the roof of the reservoir and the shade structures in various areas are photovoltaic cells used to generate power for the site.

The gardens also use a fleet of hydrogen powered utility vehicles.  The vehicles are refueled on site by a solar powered hydrogen fueling station.

Major exhibits

Origen Experience is composed of three interactive exhibits. Each exhibit is filled with stories about the Las Vegas Valley, following a natural progression from the past to the present.
Desert Living Center is a campus of Leadership in Energy and Environmental Design (LEED) buildings and features interactive galleries, educational opportunities, a design lab, and a technical training center. The Center leads by example, using sustainable architecture, interactive exhibits, and programming to show Las Vegas residents how to conserve water and live sustainably in the desert. With exhibits designed by AldrichPears Associates, architecture by Lucchesi Galati Architects, and landscape architecture by Deneen Powell Atelier, the Center won the award for Best Public Green Building Project in Nevada in 2007.
Trails – Walk four uniquely themed trails that encompass more than  of picturesque landscapes leading to a cienega.
Springs Preserve amphitheater is Las Vegas' only intimate outdoor venue surrounded by museums and gardens.
Nevada State Museum features exhibits describing the development of Las Vegas and the natural history of the area.
Nature Exchange is a small exhibit located in the desert living center. This exhibit is aimed towards children but encourages adults to participate. The Nature Exchange is a unique learning environment that relies on the personal "trading" of found natural items and information about them, to motivate interest and understanding. The trader receives points depending on the rarity and size of the object offered for trading. (One can not exchange objects from national parks, state parks, or the Springs Preserve.)
Butterfly Habitat – Wander through our unique Butterfly Habitat and witness the fascinating dance between free-flying butterflies, the plants that sustain them, and their important role as pollinators in the ecosystem.

References

External links

Official State of Nevada Tourism Site
Las Vegas Springs Preserve on Living in Las Vegas Podcast

Museums in Las Vegas
Natural history museums in Nevada
Parks in Clark County, Nevada
Urban public parks
Tourist attractions in the Las Vegas Valley


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SEO is so over, says Atlantic Digital editor
Publishers are leaving behind the era of Demand Media-style search-optimized headlines, says Atlantic Digital editorial director Bob Cohn. Social media is now driving more traffic than search to the site, and that allows editors to create headlines designed to appeal to people, not search engines.
Everyday, more and more articles are coming up stating SEO is dead. Social media is the trend for driving traffic. Will it completely super-seed SEO. For some businesses, yes. Others, no. Right now, for most businesses, you have to do both. All content below via Mashable.
"We're no longer writing to get the attention of Google algorithms. We're writing to get you to share it, to digg it," Cohn explains. I don't think this route is the way to go for most businesses, but very interesting nonetheless.
The number of online news consumers has grown consistently over the past half-decade, yet not every publication has gotten the same lift as The Atlantic, whose web audience has catapulted from approximately 500,000 to 13.4 million monthly visitors since taking down its paywall in early 2008.
As we've explored previously, there are many factors that have contributed to The Atlantic's online success: assigning a number of well-known columnists, like James Fallows and Andrew Sullivan (now of The Daily Beast), to begin writing original pieces for TheAtlantic.com; launching and staffing two new online news properties,TheAtlanticWire.com and TheAtlanticCities.com; and building up its digital ad offerings to support those hires.
Furthermore, The Atlantic is adapting its editorial strategy to the shifting landscape of online news consumption, namely, to capitalize on the growing importance of social networks, rather than search engines, as sources of traffic.
"Sixteen months ago we received the same number of monthly referrals from search as social. Now 40% of traffic comes from social media," Scott Havens, senior vice president of finance and digital operations at The Atlantic Media Company, said in a phone conversation ahead of his on-stage interview at our Mashable Connect conference in Orlando, Fla. last weekend. "Truly [our writers] are not really thinking about SEO anymore. Now it's about how we can spin a story so that it goes viral."


text:
\section{Introduction}

With the rise of sociological approaches to narrative, work in  literary criticism has increasingly turned to the ways in which authors depict social networks in their texts. This includes critical attention to both \textit{network topologies}, such as understanding characters and their structural relationships with others\ \citep{levine2009}, and \textit{information flow}, such as theorizing the representation of disease and gossip\ \citep{levine2009, margolis2012,spacks85}.
Much computational work in NLP has arisen to support the former line of research, including extracting social networks from text\ \citep{elson:2010:esn:1858681.1858696}, predicting familial relationships\ \citep{DBLP:journals/corr/MakazhanovBK14}, and modeling the interactions between characters\ \citep{Iyyer:Guha:Chaturvedi:Boyd-Graber:Daume-III-2016,Chaturvedi:Iyyer:Daume-III-2016}. This in turn has driven work in the digital humanities examining the structure of literary networks \citep{moretti2011network, algeehewitt2017,piper2017,alexander2019}.

\begin{figure}[!htb]
\includegraphics[scale=.38]{fig1.pdf}
\caption{\label{network_viz} The character co-occurrence network for \textit{Great Expectations}. Nodes represent characters and edges represent conversational interactions. Below the network, we illustrate an example of information transmission across a character triad.
}
\end{figure}

At the same time, however, there remains a substantial gap in computational work to support the latter research goal of modeling the flow of information within depicted networks.
Yet understanding how the transmission of information is represented in these imagined worlds has the potential to be of great value to scholars in the humanities, since  the resulting models can serve as a basis for broader insights about the social structures embedded in narratives, the role of characters based on  attributes such as race and gender, and the informational dynamics of gossip\ \cite{spacks82,spacks85,martin2014}.

 In this work, we specifically aim to fill this gap by developing methods to track the flow of information in novels by extracting instances of a message passing from character $A$ to character $B$ to character $C$, only given a depiction of their conversational interactions. We develop a methodology for modeling this mode of propagation in both \emph{explicit} networks (where one character provides information that is explicitly attributed to another character, such as ``Bob told me that Jack escaped''); and in \emph{implicit} networks, where information is repeated by multiple characters without such attribution. While the results of the methods enable a range of potential analyses---for instance, comparative analysis between authors, characters, and dyads---we focus on two illustrative case studies. First, we examine the linchpins of information flow---the characters who are most responsible for the propagation of information---and how they are positioned relative to the overall network topology; and second, we examine the gender dynamics of information propagation and what it tells us about how novelists represent men and women as the means and agents for transmitting facts, gossip, and other details about the social workings of these imagined worlds.

 We make the following contributions with this work:

 \begin{enumerate}
\item We present a new NLP pipeline for determining information propagation in literary texts, incorporating a range of different sub-tasks, including coreference resolution, speaker attribution, character network identification, and information extraction.

\item We present a new dataset for speaker attribution, comprised of 1,765 quotations linked to their speakers in 100 different literary texts, allowing us to evaluate a critical component of this pipeline on a wider range of literary texts than previously studied.

\item We leverage our pipeline to analyze the dynamics of information propagation in a collection of 5,345 works of English fiction from Project Gutenberg. We find that information flows through characters that fill structural holes connecting different communities, and that characters who are women are depicted as filling this role much more frequently than characters who are men.

\end{enumerate}

\section{Related work}

Much of the computational research into information propagation and diffusion has focused on the domain of social media \citep{DBLP:journals/corr/abs-1201-4145}.
Research in this area includes analyses of information diffusion in blogs \citep{gruhl2004information,leskovec2007patterns}, the spread of news across online networks \citep{leskovec2009meme}, and in particular, the spread of rumor and misinformation \citep{kwon2013prominent,friggeri2014rumor,del2016spreading,vosoughi2018spread}.

A core aspect of this work that strongly differs from networks in fiction is that the individual components of social media networks (the nodes, edges, and instances of propagation) are often directly observed. In modeling retweet dynamics in Twitter, for instance, nodes are defined as unique users, edges are directly observed friend and follow links defined by the platform, and propagation occurs when one user retweets a message posted by another they are connected to. More closely related to the challenges posed by detecting propagation in fiction is work that may directly observe the node and edge structure of a network, but must infer an act of \emph{propagation}, including work in tracking the diffusion of memes \citep{leskovec2009meme}, text reuse across legislative bills\ \citep{wilkerson2015tracing} and quotations in news\ \citep{niculae2015quotus}.

While information propagation has yet to inform work in narrative (hence the purpose of this study), network structure has increasingly informed literary scholarship.
Following the work of \citet{bourdieu1996rules},
literary scholars have in recent years begun to explore the role that social networks play both in authorial composition \citep{so2013network,mazanec2018networks} and in the narrative representation of ``networked social experience''\ \citep{levine2009}.

Treating literary works \textit{themselves} as networks, however, poses distinct computational challenges. While research into information propagation in social media tends to presume access to explicit networks, the character networks represented in novels are implicit.  To determine these networks, we draw on previous work by\ \citet{elson:2010:esn:1858681.1858696}, who build edges between character nodes through conversational interactions.
Various computational work to extract social networks from literature has built on this research over the past ten years,\footnote{See \citet{labatut} for a review.} including fundamental methods designed to extract networks for other languages like German\ \citep{jannidis2016}, incorporate other categories of nodes such as locations\ \citep{lee-yeung-2012-extracting} and objects\ \citep{Sudhahar2013}, and analyze the structure of networks to test specific hypotheses\ \citep{elson:2010:esn:1858681.1858696,agarwal-etal-2012-social,coll-ardanuy-sporleder-2014-structure,piper2017}. Our work builds on this tradition by introducing methods to reason about the phenomenon of propagation in fiction based on these constructed networks.

\section{Methods}

Our goal in this work is to investigate the behavior of information propagation in literary texts. In order to identify acts of propagation in this context, we need to determine the underlying network structure of a novel, including the nodes (by inferring characters) and the edges (by inferring some interaction between them).
We describe first our pipeline for doing so, which involves identifying a set of unique characters from their mention in a text using coreference resolution (\S\ref{s:coref}), attributing dialogue to those characters (\S\ref{s:speaker}), building a social network of speakers and listeners from that data (\S\ref{s:buildnetworks}), and operationalizing a measure of ``information'' that we can treat as an atomic unit involved in propagation using slot-based information extraction (\S\ref{s:information}).
With these constructed networks, we can measure acts of implicit propagation (\S\ref{s:implicitprop}) and explicit propagation (\S\ref{s:explicitprop}) within it.

\subsection{Coreference resolution}\label{s:coref}

Most contemporary systems for coreference resolution are trained on the benchmark OntoNotes dataset\ \citep{hovy_ontonotes:_2006}, which primarily consists of news and conversation; literature is represented there only in the  narrow genre of the Bible.

In order to use coreference resolution specifically trained on literature,  we use the coreference annotations and trained model described in \citet{bamman2019annotated}.  This model is a neural coreference system inspired by \citet{lee-etal-2017-end}, augmented with BERT contextual representations\ \citep{devlin-etal-2019-bert}, and trained on 210,532 tokens in LitBank, comprising 100 different works of English-language fiction. \citet{bamman2019annotated} report its cross-validated average F-score on LitBank to be 68.1, notably higher than the performance for a model trained on OntoNotes (which has an average F1 score of 62.9).

\begin{table*}[h!]
\centering
\begin{tabular}{|l|c|c|c|c|c|} \hline
&$B^3$&MUC&CEAF$_{\phi_4}$&Average&$\Delta$\ \\ \hline
Predicted coreference&68.0&84.9&61.0&71.3&-- \\ \hline
$\;$--Trigram matching&67.7&84.5&60.1&70.8&-0.5 \\ \hline
$\;$--Dependency parses&66.5&83.4&56.8&68.9&-2.4 \\ \hline
$\;$--Singleton mention detection&67.0&85.9&59.6&70.8&-0.5 \\ \hline
$\;$--Paragraph final mention linking&68.0&84.9&61.0&71.3&0.0 \\ \hline
$\;$--Vocatives&68.9&85.9&62.1&72.3&+1.0 \\ \hline
$\;$--Conversational pattern&66.8&85.0&58.9&70.3&-1.0 \\ \hline\hline
Oracle coreference &80.2&89.7&74.7&81.5&+10.2 \\ \hline

\end{tabular}
\caption{\label{speaker} Metrics for cluster overlap between the gold set of clusters $\mathcal{G}$ and predicted set of clusters $\mathcal{C}$.  Each cluster is defined as the set of quotations spoken by the same speaker. We also present the upper bound of carrying out speaker attribution using gold coreference labels (oracle coreference), which suggests that there is much to be gained in improving quotation attribution not only by improving coreference, but independently of it as well.}
\end{table*}

\subsection{Speaker attribution}\label{s:speaker}

\paragraph{Data.}

Previous work in literary speaker attribution has focused on a relatively small set of novels. Both \citet{he-barbosa-kondrak:2013:ACL2013} and \ \citet{muzny2017two} annotate Austen's \emph{Pride and Prejudice} and \emph{Emma} as well as Chekhov's \emph{The Steppe}. Similarly, the Columbia Quoted Speech Corpus includes six texts by Austen, Dickens, Flaubert, Doyle and Chekhov. While these datasets have been able to drive much work in the development of models for speaker attribution, they represent a comparatively narrow slice of how dialogue is depicted in literature.

In order to evaluate the robustness of models across a diverse range of novels and authors, we annotate all 100 texts in LitBank\ \citep{literaryentities} with the  boundaries for all true quotations and link each to the {entity} who spoke it. Here we are able to draw on the coreference annotations present in LitBank, which already link each mention to a unique entity. All annotations were carried out using the BRAT annotation interface\ \citep{Stenetorp:2012:BWT:2380921.2380942} by four annotators after a period of initial training, prompted to identify all quotations and attribute each one to the speaker who uttered it.  Given the high agreement rate observed by\ \citet{muzny2017two} ($\kappa$ of 0.97 for quote-speaker labels), each quotation is attributed by a single annotator. To check consistency, we double-annotate a sample of 10 texts (10\% of the entire collection) at the end of the annotation process and find a similarly high inter-annotator agreement rate (Cohen's $\kappa$ of 0.962). In total, 1765 quotations were annotated across all 100 works of fiction. This data is freely available under a Creative Commons ShareAlike 4.0 license at \url{https://github.com/dbamman/litbank}.

\paragraph{Quotation identification.}
For the task of quotation identification, we use the method implemented in BookNLP\ \citep{bammanliterary2014}, which uses simple regular expressions (text contained between an opening quote and a closing quote).  On our gold annotations, this method results in an F1 score of 90.8 for quotation identification (87.1 precision and 95.0 recall).
False positive failure cases of strings wrapped in quotation marks that do not constitute dialogue include various typographical uses of quotation for signifying other phenomena, including scare quotes for emphatic use (to introduce jargon, neologisms, or irony), titles of works of art, the mention of a term (as distinct from its use), and written use (see \citet{quote2011} for a survey). False negatives primarily arise due to regex matching errors (such as a stray quotation mark that results in an inversion of the subsequent speech and narration), or texts that do not delimit speech with quotation marks at all (such as Joyce's \emph{Ulysses}, which introduces direct speech with dashes).

\paragraph{Attribution.}

For speaker attribution, we reimplement the deterministic method of \citet{muzny2017two} using the full coreference information predicted above. \citet{muzny2017two} describes a series of deterministic sieves for the two tasks of a.) mapping quotes to the nearest speaker mention and b.) linking identified speaker mentions to character entities.  The Quote$\rightarrow$Mention phase includes sieves such as high-precision regular expressions  for predefined Quote/Mention/Verb patterns (e.g., [``\ldots,'']$_{QUOTE}$ [said]$_{VERB}$ [Jane]$_{MENTION}$), originally defined in \citet{Elson:2010:AAQ:2898607.2898769}; dependency structure information (identifying mentions that hold an \textsc{nsubj} relation to a verb of communicating); and vocatives in the previous quotation. Quotations unattributed after running all sieves are assigned the majority speaker in the context.

To separate out the task of quotation identification from quotation attribution, we evaluate quotation attribution with gold quotation boundaries. While previous work on quotation attribution in literary texts, including \citet{muzny2017two} and \citet{he-barbosa-kondrak:2013:ACL2013}, evaluate system performance using classification accuracy and precision/recall (where each quotation in a test book is judged to be assigned to the correct true speaker from a predefined gold character list), we do not presume access to a gold character list during prediction time. Like \citet{almeida-etal-2014-joint}, we evaluate performance using a measure of cluster overlap (here, the suite of metrics used in evaluating coreference resolution), where each cluster is defined by the set of quotations spoken by the same speaker.

As Table \ref{speaker} illustrates, our reimplementation of \citet{muzny2017two} for the task of speaker attribution yields an average F1 score of 71.3 across the three cluster metrics when evaluated on all 100 books in our newly annotated data.  As we ablate different aspects of the \citet{muzny2017two} model, performance generally degrades, attesting to the value of individual sieves.

\subsection{Identifying character networks}\label{s:buildnetworks}

Similar to previous approaches for determining character networks in literary fiction  \citep{Elson:2010:AAQ:2898607.2898769,moretti2011network}, we use conversation as the basis for determining the edges in our graph. However, rather than trying to identify specific speaker-listener interactions, we instead extract dialogue blocks, drawing an edge between all characters mentioned outside of a quotation in a given block. Edges are weighted by the total number of dialogue blocks in which a given pair of characters are found to be co-present. We use a simple heuristic to identify these conversation blocks: if three or more contiguous sentences do not contain any quoted dialogue, we treat this as the termination of the block. The resulting graph serves as the basis for identifying information propagation in a given novel, as detailed in the following subsections.

\subsection{Defining information}
\label{s:information}

Whereas large-scale corpora such as social media data sets provide networks in which fuzzy matching of information may be appropriate and in which information repetition is often substantial\ \citep{leskovec2009meme}, in the context of novels such methods are unlikely to have sufficient precision. As a result, we select an information approach which allows us to maximize precision at the cost of  potentially missing some instances of propagation. Our approach entails identifying quoted speech that references at least one character. One way to define this type of speech would be to simply describe it as \textit{gossip}, though we feel that this is an overly narrow term given the general nature of our approach. Specifically, we select propositional tuples of the form (subject, verb, object), such that the subject holds an \texttt{nsubj} dependency relation to the verb and the object holds an \texttt{obj} relation (using the terminology of the Universal Dependencies\ \citep{nivre2016universal}); the subject and object may be filled by a character entity, a non-character nominal phrase, or a null token if neither is present. We ignore any tuples which contain \textit{I}, \textit{you}, or \textit{we} (along with their variants) in either the subject or object slots, since they have comparatively higher errors in coreference. For the verb slot, we always select the lemma form of the proposition's head verb.  Character entities in a proposition are identified by their unique character IDs established through coreference resolution (and not by the surface form of their mention).

Consider the following hypothetical example:
\\[1pt]
\begin{center}
\begin{dependency}[theme = default,
label style={font=\bfseries,thick}
]
   \begin{deptext}[column sep=-.3em, row sep=.0ex]

Bob \& punched \& Tom \& and\& he \& left \\
   \end{deptext}
   \depedge{2}{1}{nsubj}
   \depedge{2}{3}{obj}
    \depedge{6}{5}{nsubj}
\end{dependency}
\end{center}

Given the operation of coreference resolution  mapping ``Bob'' and ``he'' to the entity \texttt{Bob-id1} and ``Tom'' to \texttt{Tom-id2}, the extracted tuples for this sentence would be: [\texttt{Bob-id1}, punch, \texttt{Tom-id2}] and [\texttt{Bob-id1}, leave, $\emptyset$]. We extract all propositional tuples using a set of rules applied to the dependency parse of a given sentence. Although reductive to some degree, defining and extracting information in this way allows us to avoid informational noise and only select consistent propositional units.

To further reduce potential informational noise, we also only select tuples containing words that are likely to have some intrinsic interest to the plot and which have a relatively fixed meaning. After analyzing the 100 words that occur most frequently across all the tuples extracted from our corpus, we select tuples containing terms associated with the following four categories: \textit{amorous}, \textit{hostile}, \textit{juridical}, and \textit{vital}. For each category, we include the following words along with any synonyms that are also present in the top 100 tuple words: \textit{amorous} (love, marriage), \textit{hostile} (hurt, hit, shoot, kill), \textit{juridical} (arrest, escape, innocent, guilty), and \textit{vital} (alive, sick, dead).  Since the Gutenberg corpus primarily contains nineteenth-century novels, these topics reflect many of the key events that these works of fiction tend to focalize.

\subsection{Defining implicit propagation}
\label{s:implicitprop}

We identify instances of implicit information propagation simply by determining whether a propositional tuple passes between a minimum of three characters. In other words, we look for an informational triad of the form character $A$ $\rightarrow$ character $B$ $\rightarrow$ character $C$, such that character $A$ and character $B$ are co-present when character $A$ voices the initial instance of the proposition (but character $C$ is not), and character $B$ and character $C$ are co-present when character $B$ repeats the proposition during a different conversation block.

\subsection{Defining explicit propagation}
\label{s:explicitprop}

Along with implicit instances of information propagation, we note that novels often contain explicit propagation as well. We define explicit propagation as occurring when a character reports what another character said to a third character. In other words, we simply search for  variations of the pattern ``\texttt{[character-id]} said" in the context of quoted speech. Specifically, the variations considered include synonyms for ``say" along with any arguments or modifiers that are relevant to introducing reported speech (e.g., ``declared," ``told me," ``mentioned that," ``claimed to," etc.).

The benefit to capturing instances of explicit propagation is that such instances can be extracted with very high precision regardless of the informational topic being discussed. Consequently, unlike for implicit propagation, we make no constraints on the nature of the information itself (in contrast to the four topics defined above). After identifying instances of explicit propagation, we incorporate coreference resolution and speaker attribution to determine the specific characters of a given propagating triad.  Section \ref{explicit_analysis} discusses how the resulting data from this approach can be used to analyze the role that gender plays in the depiction of information propagation within novels.

\section{Experiments}
\label{experiments}

Given instances of information propagation extracted from novels, we seek to understand the structural roles of the literary network and its topography that contribute to information passing between dyads.  In particular, we seek to disentangle two possible alternatives:

\begin{itemize}
    \item[H1.] Information propagates through  bridges who pass information between otherwise disconnected communities.
    \item[H2.] Information propagates among densely connected strong ties (such as between members of the same family who interact frequently).
\end{itemize}

These alternatives correspond to different functions of gossip in literature, as theorized by \citet{spacks85}: while gossip primarily involves the ``deliberate circulation of information,'' it also functions to reinforce existing relationships among strong ties (a point taken up in real-world social networks in \citet{foster2004}).
We operationalize this distinction for understanding the dynamics of {implicit propagation} by describing information-propagating characters and non-information-propagating characters through six network measures that capture their topological properties within the network:

\begin{enumerate}

\item \textbf{Closeness centrality}: the average inverse distance between a given node and all other nodes in a graph.
\item \textbf{Betweenness centrality}: the fraction of shortest paths that pass through a node, summed over all node pairs.

\item \textbf{Average neighbor degree}: the average degree of the nodes in a given node's neighborhood.

\item \textbf{Effective size}: the measure of the non-redundancy between a node and its contacts---specifically how connected a node's contacts would be in its absence (i.e., the resulting structural hole).

\item \textbf{Efficiency}: the effective size of a node divided by its degree.

\item \textbf{Triangle count}: the number of triangles for which a node serves as a vertex, where a triangle is defined as a set of three nodes that are directly connected to each other.

\end{enumerate}

We use the above measures to describe all nodes that either function as the $B$ node in an $A\rightarrow B\rightarrow C$ information triad, or could function as such a node. More specifically, whenever we observe an instance of propagation $A\rightarrow B\rightarrow C$, in which at least one separate character $B'$ was co-present with $B$ when hearing $A$'s information (but did not propagate it further), we select a pair comprised of $B$ as a propagating node and $B'$ as a non-propagating node (sampling $B'$ from the set of co-present characters if more than one was present). In cases in which no non-propagating character was co-present, we instead sample a $B'$ from the set of propagating instances that had multiple co-present characters.   When sampling the non-propagating $B'$ nodes, we only select characters that have been observed to speak at least once in the text based on our speaker attribution model (we hypothesize that selecting these characters is a better way to judge the efficacy of a propagation model, since they at least vocalize some information in the narrative, and hence are more likely to resemble propagating nodes in terms of their narrative functions).

\subsection{Results}

In order to test implicit information propagation in literature, we run tuple extraction on 5,345 works of fiction from the Project Gutenberg corpus. We find that roughly 3,600 of these books contain at least one instance of a repeated tuple containing a word from our four topics of interest (indicating the possibility of propagation based on our criteria). We proceed to run the rest of our pipeline on this subset of books.\footnote{We process all books on a high-performance computing cluster using 24-core Intel Xeon Haswell processors and 64 GB of RAM; the average runtime of this pipeline on one book on this platform is five minutes and two seconds.} In total, we find that 35\% of these works contain at least one instance of implicit information propagation.

To distinguish between the two hypotheses outlined above, we scale all the features of the  data between 0 and 1 and train a non-regularized logistic regression model to distinguish between information propagating $B$ nodes and non-propagating $B'$ nodes. We run the model on 1,730 $B$ nodes and 1,730 $B'$ nodes.  The results are shown in Table \ref{graph_measures} and discussed in more detail in the next section.

\begin{table}[h!]
\centering
\begin{tabular}{|c|c|} \hline
\textbf{Graph Measure} & \textbf{Coefficient}  \\ \hline
Efficiency &$\phantom{-}3.0^\ast$   \\ \hline
Effective size &$\phantom{-}2.7\phantom{^\ast}$   \\ \hline
Betweenness centrality &$\phantom{-}0.5\phantom{^\ast}$  \\ \hline
Closeness centrality &$\phantom{-}0.1\phantom{^\ast}$  \\ \hline

Triangles &$-0.4\phantom{^\ast}$\\ \hline

Average neighbor degree &$-4.9^\ast$    \\ \hline
\end{tabular}
\caption{\label{graph_measures} Logistic regression model coefficient values. Stronger positive values are indicative of information-propagating nodes; stronger negative values are indicative of non-propagating nodes. $^\ast$ denotes $p < 0.01$.}
\end{table}

To ensure that our results are not simply caused by aspects of each  network's general topology (irrespective of the unique qualities of propagating $B$ nodes) we also run a degree-preserving randomization experiment\ \citep{10.1007/978-3-642-21286-4_10} as a more stringent means for testing significance. For each network containing a propagating node, we generate 10 expected degree graphs and use them to calculate network measures for the corresponding propagating $B$ and non-propagating $B'$ nodes in the original network, producing a set of 10 randomized measures for each of the 1,730 original nodes in each class. We then randomly sample a single measure from each of these sets, yielding 1,730 randomized node measures for both classes, and re-run our logistic regression model on that resample. We repeat this process 10,000 times to generate an expected null distribution for each coefficient and assess the frequency with which a null coefficient value was observed to be as extreme as the value we observe under the true network---analogous to a p-value in a bootstrap hypothesis test\ \citep{efron1982jackknife,berg2012empirical,dror-etal-2018-hitchhikers}.

For the two node measures found to be significant under our original model, efficiency has a p-value of 0.08 (8\% of 10,000 random trials observe a statistic as extreme as $3.0$), no longer rising to the level of significance at $\alpha=0.01$, while average neighbor degree has a p-value of 0 (no random trial sees as a statistic as extreme as $-4.9$), providing further evidence of its significance as a feature for discriminating information-propagating nodes.

\section{Analysis}

\subsection{Implicit propagation and weak ties}

As Table \ref{graph_measures} shows, average neighbor degree and efficiency are both found to be significant at a threshold of $\alpha=0.01$, while average neighbor degree is confirmed to be significant under a degree-preserving randomization experiment. These results support the first of our two postulated hypotheses (introduced in \S \ref{experiments}): information in novels propagates through characters that serve as bridges between otherwise disconnected communities.

Average neighbor degree has the largest coefficient (by absolute value) and is negatively correlated with propagation.
High values of average neighbor degree denote communities that are already well-connected (both to each other and to the rest of the network).
In such a information-rich neighborhood, instances of propagation would be of less value or necessity, and hence would be less likely to be observed.

Support for the first hypothesis is further confirmed by the strong positive coefficient for efficiency. Like effective size, efficiency is a means of determining the extent to which a structural hole would occur if a specific node were removed from the network. Whereas effective size indicates the possibility of such a structural hole in general, efficiency measures how much each one of a node's connections  on average contribute to linking otherwise disconnected neighborhoods. Thus high efficiency suggests that a node is not only serving as a useful bridge between other nodes, but that it is doing so productively relative to its total number of connections.

In a sense, these results suggest that we are observing a version of the weak tie theory first proposed by \citet{granovetter1973strength}. By virtue of the fact that a character's connections are not themselves closely connected, that character can in turn serve an essential informational role for the community.

\subsection{Explicit propagation and gender}

While our methods for extracting implicit propagation for amorous, hostile, juridical and vital events identified 1,730 instances in 5,345 novels, our method for identifying explicit propagation yields far more---93,948 instances of propagation involving 258,619 triads (since there may be multiple listeners for a single instance). Although the analysis carried out on implicit propagation is not possible for the explicit case (since there is no way to identify co-present $B'$ nodes when the initial instance of a proposition remains unobserved in the text), the size of the explicit results are conducive to other analyses. Specifically, we consider here the role that gender plays in the depiction of propagation.  As \citet{spacks85} points out, women are often stereotyped (both within the real world and in representations in literature) as more likely to engage in gossip; from a networked perspective, they are also often cast as intermediaries between men, ``serving as points through which to triangulate male-to-male desire or power'' \citep{Selisker2015}.
Analyzing gender (and other demographic attributes) in the context of information propagation enables scholars to consider how authors construct the informational ecology of their novels given the functional roles played by different characters.

\label{explicit_analysis}

To measure the role that gender plays in how authors represent information propagation in novels, we calculate the relative proportion of different gender configurations for propagating triads compared to all triads present across our entire data set (we determine the gender of a character by counting up all the male and female nouns and pronouns in that character's coreference chain). This allows us to answer the question: given the overall structural opportunity to transmit information, how often does transmission actually occur based on gender?

\begin{figure}[!htb]
\begin{centering}
\includegraphics[scale=.65]{gender.pdf}
\caption{\label{gender} Comparison of the relative proportions of triad variations based on gender. All triads (light blue, $n=$ 158,250,238) represent every observed triad across 5,345 books. Propagating triads (dark blue, $n=$ 258,619) indicate only those triads observed to explicitly propagate information.  The widest 95\% confidence interval across all proportions is $\pm 0.0018$, so that all differences within a gender triad type are significant.}
\end{centering}
\end{figure}

Figure \ref{gender} illustrates the proportion of each gender configuration compared to the total; for instance, while 15.4\% of all character triads are comprised of three women (\texttt{F-F-F}), 20.1\% of triads involved in information propagation involve three women. Overall, we find that not only are female characters more likely to serve as propagators than male characters in this dataset, but that female characters fill this role more frequently than one would expect given the proportion of female connector nodes across all triads.  The proportion of propagation when the middle node is male, conversely, is lower than the expected value for every configuration. In other words, authors represent women as information propagators comparatively more frequently than men relative to their overall expectation.

Although literary criticism tends to envision the role of women in novels as being intermediaries between men\ \citep{woolf1929,Sedgwick1985, schantz2008,Selisker2015}, our analysis of information propagation tells a slightly different story. While women do indeed appear to serve as intermediaries/connectors more frequently than men do, women  propagate information \emph{between} men much more rarely than they do in other configurations (i.e., \texttt{F-F-F}, \texttt{F-F-M}). Though women may of course still connect men in these narratives, they do not appear to do so by notably passing on information.  We leave determining the broader significance of this result to future work.

\section{Conclusion}

We introduce the task of identifying information propagation in literary social networks, designing an NLP pipeline for extracting both \emph{implicit} and \emph{explicit} propagation.  This work offers a new perspective on the analysis of social networks in literary texts by considering the dynamics of how information flows through them---both as a result of the structural topology of the network (characters who successfully propagate are information bridges between communities), and as a result of the specific characteristics of each node (women are depicted more frequently as successful propagators than men).

This study, of course, contains limitations: readers of fictional works are only afforded a partial perspective of the world that is represented---namely the interactions an author chooses to describe (and not, for example, the dialogue we might presume takes place ``off-screen"). Considered from a narratological perspective, however, this is a benefit rather than a drawback, since our goal is not to understand the underlying reality of these imagined worlds but rather how authors opt to represent the informational dynamics from which their stories are constructed.  In developing this pipeline to examine how authors depict the transmission of information within narrative texts, we hope to drive a variety of future research in this space, including not only such narratological questions as how ``gossip impels plots''\ \citep{spacks85}, but also questions pertaining to issues of bias in representation, the flow of information, and factuality. Code to support this work can be found at \url{https://github.com/mbwsims/literary-information-propagation}.

\section*{Acknowledgments}

Many thanks to Olivia Lewke, Anya Mansoor and Sam Levin for research support and to Lucy Li, Richard Jean So and the anonymous reviewers for valuable feedback on this work. The research reported in this article was supported by funding from the National Science Foundation (IIS-1942591) and the National Endowment for the Humanities (HAA-256044-17), and by resources provided by NVIDIA.

\bibliographystyle{acl_natbib}

redpajama_set_name: RedPajamaArXiv


text:
Eur J Pediatr. 2008 Feb;167(2):133-9. Epub 2007 Aug 1.
What is new in pertussis?
Bamberger ES1, Srugo I.
Bnai Zion Medical Center, Haifa, Israel. esbamberger@yahoo.com
Despite high vaccination coverage, over the last fifteen years there has been a worldwide resurgence of B. pertussis infection. While classical pertussis in the prevaccine era was primarily a childhood disease, today with widespread vaccination, there has been a shift in the incidence of disease to adolescents and adults. Centers of Disease Control and Prevention (CDC) data from 2004 reveal a nearly 19-fold increase in the number of cases in individuals 10-19 years and a 16-fold increase in persons over 20 years. Indeed adolescent and adults play a significant role in the transmission of pertussis to neonates and infants who are vulnerable to substantial morbidity and mortality from pertussis infection. Several explanations have been proposed to explain the increasing incidence of disease, with waning immunity after natural infection or immunization being widely cited as a significant factor. Improving molecular biology diagnostic techniques, namely PCR assays, also accounts for the increasing laboratory diagnosis of pertussis. Expanding vaccination strategies including universal immunization of adolescents, targeted immunization of adults, and in particular, healthcare workers, childcare providers and parents of newborns, will likely improve pertussis control. With pertussis continuing to pose a serious threat to infants, and greatly affecting adolescents and adults, there remains a need to: (a) increase the awareness of physicians as to the growing pertussis problem, (b) standardize diagnostic techniques, and (c) implement various new vaccine strategies to enhance its control.
Bordetella pertussis*
Whooping Cough*/diagnosis
Whooping Cough*/drug therapy
Whooping Cough*/epidemiology
Whooping Cough*/prevention & control
Whooping Cough - MedlinePlus Health Information


Brazil, 18th Century. The colony of Portugal endures a decline in gold production. A Portuguese minority of corrupt and nepotistic administrators rules over a society formed mostly by African slaves, local Indians and mestizos. Joaquim is an efficient military famous for capturing gold smugglers. He longs to be compensated with a Lieutenant promotion to buy the freedom of a slave he is in love with. But the promotion never comes through. Joaquim despairs. A risky mission to the feared Badlands in search of new mines seems to be the only way to achieve the longed-for promotion. Joaquim returns from the mission with a load of precious stones but his achievement is undervalued. Out of frustration, a revolutionary emerges.


text:
MJF compared to Roddy Piper as critics rave over "Pipe Bomb" promo
Who do you think would win a battle on the microphone between MJF and Roddy Piper?
MJF cut one of the promos of his career amidst a host of controversies as he made this weeks AEW Dynamite episode all about him.
Although he was squashed by Wardlow at AEW Double or Nothing 2022, he came out looking better than ever on the week following. He stormed to the ring as passionate as ever, grabbing the microphone before running down Tony Khan like nobody ever had before.
He bemoaned his lack of comparative pay in AEW, claiming he was being paid less than the former WWE guys like Christian Cage, Mark Henry and Jeff Hardy who have recently signed for the company. His anger was clear and real as he claimed he was the second biggest ratings draw in the company (being CM Punk presumably) and how he should be paid more because of it.
He soon walked out of the arena after having his mic cut off, resembling CM Punk's famous Pipe bomb promo. The segment ended and in the commercial break MJF was confronted by CM Punk, seemingly setting up CM Punk vs MJF some time in the future for the AEW Championship.
While comparisons to CM Punk's Pipe Bomb running rife, some wrestling personalities and journalists have sought to draw comparisons between MJF and Roddy Piper, as he imitates one of the all time greats in his bid to reach the top of All Elite Wrestling.
MJF and Roddy Piper
MJF and Roddy Piper have always been a duo that have been drawn into comparisons. MJF has previously revealed that Roddy Piper is his favourite wrestler of all time and a man who he used to help form his character in AEW.
"And if I was able to have an opportunity to have promos back and forth with Roddy, and then a match that I know would be vicious as all hell. I think that would probably go down as the greatest match in the history of professional wrestling."
With his obvious admiration for the Hot Rod, MJF will be pleased with his comparisons to the WWE Hall of Famer. Roddy Piper was one of the best heels in wrestling history and had a command on the mic that few wrestlers have ever been able to achieve.
MJF is one of the few that can match Roddy Piper in his prime on the microphone. MJF's promos in AEW have been some of the greatest in wrestling history, with his tale of his journey in wrestling leading up to his match with CM Punk being arguably the greatest of all time.
Jim Cornette has compared MJF to Roddy Piper a number of times, based on his promo ability, his resolution to sticking to character no matter what and his general genius on the microphone, one of Piper's greatest traits.
During and episode of his podcast The Jim Cornette Experience, Cornette compared MJF to Roddy Piper by saying;
"I think [Piper] would love [MJF] and he would love it. I think he would say here is a guy that can talk his ass off, knows how to push people's buttons, likes to get heat… If he saw his matches, especially the one with Darby Allin I think he would be very impressed with that.
"So yeah, I've got to think that Roddy would be relieved that somebody has come along like that and still obviously takes the business fairly seriously and can go out and not do the same style that Roddy did, but go out in the same vein at riling people up on the microphone like Roddy did 40 years ago. I think he would approve wholeheartedly"
Sports Journalist Bill Simmons although thinks of MJF as on the level of Roddy Piper. In response to MJF's promo running down Tony Khan on AEW Dynamite, Simmons proclaimed on Twitter that "MJF is unbelievable. This is mid-80's Roddy Piper level."
MJF is unbelievable. This is mid-80's Roddy Piper level. https://t.co/N7xgrzufl7
— Bill Simmons (@BillSimmons) June 2, 2022
While MJF has a long way to go before he can be held in the same regard as Roddy Piper, if he carries on the way he has progressed at the age of just 26, he will have no issues being named alongside the all time greats like Hot Rod once his career is all said and done.
Tags MJFTony KhanWARDLOW
AEW All Out 2022 set to be MJF vs CM Punk?
Who is Jay White's opponent for AEW x NJPW Forbidden Door?