By Lorela U. Sandoval – – SENDING balikbayan boxes to relatives in the Philippines has been a lifelong tradition for Filipinos abroad and a tangible reminder of their love to families and friends back home. It is, however, fraught with problems and risks – ranging from late delivery and damage to the shipment to pilferage and missing or lost cargo.
The Department of Trade and Industry (DTI) has received many complaints regarding balikbayan boxes over the years. Based on data provided by Eduardo Quizon of the Philippine Shippers' Bureau of DTI, the countries with the highest number of balikbayan boxes sent to the Philippines are the Saudi Arabia, United Arab Emirates (UAE), Hong Kong, Kuwait, Qatar, Taiwan, Singapore, Italy, United Kingdom (UK), and Korea.
Latest data from DTI show that the countries with the highest number of cases or complaints were UAE (174 cases) USA (170), KSA (112), Kuwait (46), Singapore (25), UK (21), and Hong Kong (10).
Dubai in UAE has the highest number of recorded cases at 167, prompting the DTI to label it a "hotspot". In the USA, Chicago is tops at 52, while in KSA, it was Riyadh at 51.
The DTI recorded 73 complaints in 2010, 212 in 2011, 168 in 2012, 173 in 2013, and 45 as of June 2014.
UPDATE January 15, 2015 – The Department of Trade and Industry placed 43 foreign and local sea freight forwarders on its blacklist after shippers and consignees lodged numerous complaints against the firms, mainly the non-delivery of balikbayan boxes.
The complete list of the blacklisted freight forwarders and consolidators, and those that have pending cases are uploaded on the DTI's website (http://www.dti.gov.ph/dti/index.php/resources/listings).
What does the agency do to address complaints involving balikbayan boxes? In an interview with Planet Philippines, Quizon explains that the agency follows a standard operating procedure when dealing with the complaints.
"When we receive complaints, we subject them to mediation, but first we need a formal written complaint supported by bill of lading, packing list, waybill, or invoices to validate their complaint," said Quizon, who is DTI-PSB's Shipping Operations Specialist II and recently Acting Consumer Adjudication Officer.
He cited an instance wherein two water heaters for a swimming pool were missing in a box that came from the US West Coast. To prove the pilferage complaint, the complainant took pictures of the fist-size gaping hole in the box.
If a freight forwarder is found to be scamming Filipinos overseas of their balikbayan boxes, the Fair Trade and Enforcement Bureau (FTEB) may impose monetary fines, suspension or cancellation of accreditation.
"Once we find out they have liability there is a fine of P50,000 for first offense. Second offense, there is a cease and desist order. Third is closure," said.
If a settlement is reached between the forwarder and the complainant, the forwarder has to pay specific fees to the complainant and to the DTI office as well for culpability.
Quizon said that if the complainant wants to pursue a criminal case against the forwarder, he should file a case before a regular court. The DTI handles only administrative cases.
To lessen cases of Filipinos getting victimized and curb the malfeasance of some freight forwarders, the DTI regularly holds roadshows and information drives abroad to educate overseas Filipinos on how to protect their shipments.
Quizon said the roadshows have been very successful in imparting practical tips and information to overseas Filipinos, particularly the OFWs who are usual victims of unscrupulous forwarders.
• Book your balikbayan boxes only with cargo consolidator/freight forwarders accredited by the Fair Trade and Enforcement Bureau if by sea or the Civil Aeronautics Authority of the Philippines if by air. Senders can check the consolidator/forwarder's name on www.dti.gov.ph under the section of the Consumer Welfare and Business Regulation or through the Philippine Consulate office overseas.
• Monitor regular advisories and alerts at www.dti.gov.ph.
• Organize your box and declare your shipment by completing a detailed list of the contents and their value. If possible, include the description and brand of each item. Attach one copy of the list outside the box; place another copy of the list inside the box so the recipient can ascertain of any item is missing.
• Secure transport or shipping documents such as official or cargo receipt for fees paid, and a Bill of Lading, which is a document issued by a transportation carrier to the shipper as proof that they received the shipment of goods and placed them on board a particular vessel for delivery to a particular destination.
• Get the complete name and contact details of the forwarder's agent in the Philippines, and ensure that such information are indicated in the official receipt or transport document.
• Monitor the movement of your cargo from the point of origin to destination to ensure proper shipment and delivery.
• Inform your consignee to check your cargo with the agent in the Philippines even before the cargo arrives.
• For cases of pilferage, lost item, or non-delivery, file an immediate claim or complaint with the customer service of the freight forwarder. You may also file a complaint directly to the Philippine Shippers Bureau at 2nd Floor Department of Trade and Industry Bldg. 361 Gil Puyat Avenue, Makati City, or call the DTI-Direct Hotline 02-7513330.
THE Bureau of Customs has launched an online tracking system that will enable recipients of balikbayan boxes to check on the status of their packages.
"We have received several complaints from families of overseas Filipino workers blaming the bureau for their lost boxes," he said.
To access the balikbayan box tracker, go to the Bureau of Customs website (http://customs.gov.ph) and click the "Balikbayan Box Tracker" banner. The tracker also contains the name of the foreign forwarder, name of the local forwarder or broker, entry date filed, date cleared, and the current status (of the shipment).
Customs officials inspect balikbayan box shipments.
"Families expecting balikbayan boxes should know the name of the forwarder and the bill of lading number to be able to track the shipment where the box is included," Sevilla explained.
Balikbayan boxes sent from abroad are "usually consolidated into batches and placed in one container van aboard a cargo vessel bound for the Philippines.
Each container van has about 400 balikbayan boxes. Shipments from Asia typically arrive here in 15 to 20 days while those from North America or Europe arrive in 55 to 65 days. The local cargo forwarder handles the customs clearance of the entire shipment, as well as the delivery of each box to the intended recipient in the country, the BOC public information and assistance office.
"Simultaneously, the tracker can serve as a venue for accredited Philippine sea freight forwarders to police their own ranks and prevent questionable acts and dealings," said DTI Consumer Protection Group Undersecretary Victorio Mario Dimagiba.
To date, the DTI's Fair Trade Enforcement Bureau has accredited 654 Philippine sea freight forwarders. An estimated 5.5 million balikbayan boxes are sent to the Philippines each year, about 40 percent of which arrive here from September to December.
About 65 percent of the shipments are received at the Manila International Container Port while the rest are shipped through the Port of Manila, as well as Cebu, Davao and the Subic Freeport in Zambales.


There's been a change of schooling scenery for the new students at Pestalozzi this year as Claremont Senior School is now their place of study.
The privately run school which achieved 'Outstanding' in every category from Ofsted will give these bright, enthusiastic and determined young men and women the opportunity to change their lives for the better.
It's been a little over a month since the switch and it's gone down really well with the students.
Ruvimbo loves Maths and is enjoying a different learning environment compared with what she was used to in Zimbabwe. Back home the classes were filled with lots of students which meant that one-to-one teaching time was often not forthcoming. But at Claremont the classes are smaller and that vital one-to-one interaction with the teacher is something that Ruvimbo loves.
As for Nischal Poudel from Nepal, who has only been at Pestalozzi for two weeks, going to Claremont has been a wonderful experience so far.
"It's great! The subjects on offer are wide ranging and very interesting," explained Nischal.
The extra-curricular activities are a big part of Claremont's overall ethos and Nischal has made the most of these exciting opportunities.
"I do football, swimming and computer science. You can also study French, German or Spanish which is amazing" said Nischal.
Ruvimbo and Nischal are both excited to be continuing their studies at Claremont. If you want to keep up with what the students are doing and ways you can donate please look out for more stories on the website and on our social media channels.


text:
Learning to live without my mother
Courtesy of TheWeek.com| By Neda Semnami | Originally Published 05.11.2013 | Posted 03.23.2018
It had been a long winter of epic storms. Spring was coming.
It was that sweet time in D.C. when the trees erupt in an annual pink-blossomed explosion. But I was inside strong-arming my mother, bullying her to put on a dress and take a drive with her brother and sister.
"You're beautiful," I said.
"I am?"
"Oh, Nedu. I'm not," she said.
My mother was dying. And she was all bony elbows, jutting clavicles, and sunken cheekbones.
Still, she was as beautiful as she was tired and — God, my poor Mom — that day she was exhausted. She would have been more comfortable sleeping with a fresh Dilaudid patch, but I was determined to cheer up a woman who, whether we knew it or not, had only a week left to live.
That day, I wheeled Mom into the bathroom. I brushed her hair. Changed her bandages. Doled out pills. I rubbed lotion over her chest and back, down her arms. I carefully applied a bit of lipstick and pinned her curls from her face.
For some reason, I thought that maybe after the makeup, my lectures, some jokes, Mom would feel better. That her one cloudy eye might clear up and she would hang on for a bit longer.
I thought that if she was happy, and the springtime was beautiful enough, she might not leave us. She might not be so tired and my family might have just a little more time together.
It was a perfect spring afternoon when my aunt and uncle helped Mom into the car. The three of them set off for what I remember as the last time they had together, just the three of them. It took a superhuman kind of control not to run after them, throw my arms around her neck, and beg her not to leave me behind for the afternoon — to beg her not to leave me ever.
Or maybe, I thought, she will come back happy, refreshed. Maybe all she needed was to get out of the house, to take a drive, to put on lipstick.
But it turns out that when a person is dying, it isn't a thing that springtime or family or lipstick can cheer into reverse.
The day after her drive, or maybe the day after the day after, I walked upstairs and saw Mom sleeping on the adjustable hospital bed set up in the living room. I looked over at my uncle reading on the couch and, suddenly it hit me just how incredibly, stupidly, laughably young and unprepared I really was for Life Without Her.
I crawled into the bed hospice had set up under the skylight. I started crying. Then the crying turned to weeping and the weeping turned to sobs. The world, and my grief, were too much. I was just a small little nothing of a human, and the whole dying Mom scenario felt kind of like the Universe was personally challenging me.
Then the sobs gave way to howls.
Mom's eyes, half-blind after years and years of cancer, flew open.
"Nedu," she said, her tone alarmed, her voice a whisper. "Who died?"
"No one, yet," I said.
We looked at each other and I laughed. And she laughed. And we held hands and kissed each other and she told me I was going to be okay.
"Mu, how much do you love me?" I asked.
"Nedu," she said. "I love you as big as the universe, as wide as the ocean and back, forever and always."
"Mu," I said. "I love you as big as the universe, as wide as the ocean, and back, forever and always."
That's when I knew, in that precise moment, that chapter of my life — one where I could always go home, where I got to be a daughter — was over.
That was the last moment I was mothered.
It's been three years since she left, and not one person has made me laugh like Mom. No one has made me as angry. Without her, no one has made me feel as safe or as loved as she could, as she did on that afternoon in the hospice bed.
And yet… I have learned there's a heady freedom that comes with the loneliness of being orphaned.
My mother, who spent her career as a remorseless editor, would have been startled that I have become a serious writer — and it's a career I might not have pursued if Mom was still alive. The uncertainty of the career would have had her concerned. She would have demanded that I spend more time being practical. There would have been liberally administered critiques and some loudly voiced concerns over my having "enough" talent to "make it." This would be, as it often was, a source of some tension between us.
But as the years pass, Mu's nitpicking voice fades. It is steadily and instinctively being replaced by my own.
And there is a little part of me that luxuriates in being in charge, not unlike the way people say that inmates celebrate when they are given the keys to the asylum.
I lost my Dad when I was young. An unfortunate series of events led to his execution, after a relatively lengthy imprisonment, at the hands of the Islamic Republic of Iran. I barely knew my father. I was a toddler when he was arrested and taken from us, and the grief followed me for years.
In the months leading up to Mom's death, I told myself that this time would be different. With Mom, I was determined to just spend an efficient amount of time being sad, followed by a contentment and inner peace. I would grieve, I thought, and then it would be over.
That's not how grief works.
Three years on, some days it's all I can do to wake up, walk the dog, do my job, laugh at jokes, have a moment, pay my bills, go to sleep, and get ready to do it all again.
Three years on, there is a steady monologue looped in my head: Do I have to be a grown-up forever? I feel broken. I want my Mom today. I want my Dad today. I'd like to go home today. What if I can't feel it all without folding in on myself completely?
I think all of this as I wait in line for coffee on Friday morning or as I get pump gas on a Wednesday after work or as I race to meet a Thursday deadline or while I have a quiet drink with friends on Monday night.
As I go about my day, Old Lady Grief tails me. As I get ready for bed, I say to the old bag: Go away. I don't want you.
I just want my Mom.
The author and her mother in Washington, D.C. in 2009.
Caregiving, Caring for a parent, Death and Dying, Talking about dying
One thought on "Learning to live without my mother"
Savoring Sixty and Beyond

redpajama_set_name: RedPajamaCommonCrawl


text:
2018 DGA Awards Nominees Include Jordan Peele, Greta Gerwig, Guillermo del Toro, More
By eelyajekiM | @ | Thursday, January 11th, 2018 at 9:15 pm
Awards season is already in full swing with the HFPA having awarded the Golden Globes this past weekend, and the LAOFCS announcing their winners last month. Often times, they are an indicator of who will end up being nominated or winning an Academy Award. However, actors, writers, producers, and directors in respective guilds and societies have their own awards that recognize its members. We've already seen nominations from SAG, WGA, and PGA, and now the Directors Guild of America have released their own nominees for film and TV.
Among those honored are Guillermo del Toro for The Shape of Water, Greta Gerwig for Lady Bird, Jordan Peele for Get Out, and Aaron Sorkin for Molly's Game. There's also Christopher Nolan for Dunkirk and Martin McDonagh for Three Billboards Outside Ebbing, Missouri. Check out the full list below.
Here are the nominees for directors in film.
Outstanding Directorial Achievement in Feature Film
Guillermo del Toro – The Shape of Water
Martin McDonagh – Three Billboards Outside Ebbing, Missouri
Outstanding Directorial Achievement of a First-Time Feature Film Director
Geremy Jasper – Patti Cake$
William Oldroyd – Lady Macbeth
Taylor Sheridan – Wind River
Aaron Sorkin – Molly's Game
Outstanding Directorial Achievement in Documentary
Ken Burns & Lynn Novick – The Vietnam War
Bryan Fogel – Icarus
Matthew Heineman – City of Ghosts
Steve James – Abacus: Small Enough to Jail
Errol Morris – Wormwood
I won't knock the DGA for recognizing Greta Gerwig, something the HFPA wouldn't do, but you'd think the DGA would honor her with the Outstanding Directorial Achievement of a First-Time Feature Film Director just like Jordan Peele. He also was recognized for Outstanding Directorial Achievement in Feature Film. However, at least she got top honors, which could end up helping her get a nom for Best Directing at the Academy Awards.
Now here are the nominees for TV.
Outstanding Directorial Achievement in Dramatic Series
The Duffer Brothers – Stranger Things, "Chapter Nine: The Gate"
Reed Morano – The Handmaid's Tale, "Offred"
Jeremy Podeswa – Game of Thrones, "The Dragon and the Wolf"
Matt Shakman – Game of Thrones, "The Spoils of War"
Alan Taylor – Game of Thrones, "Beyond the Wall"
Outstanding Directorial Achievement in Comedy Series
Aziz Ansari – Master of None, "The Thief"
Mike Judge – Silicon Valley, "Server Error"
Melina Matsoukas – Master of None, "Thanksgiving"
Bethany McCarthy-Miller – Veep, "Chicklet"
Amy Sherman-Palladino – The Marvelous Mrs. Maisel, "The Marvelous Mrs. Maisel"
Outstanding Directorial Achievement in Movies for Television and Mini-Series
Scott Frank – Godless
Barry Levinson – The Wizard of Lies
Kyra Sedgwick – Story of a Girl
Jean-Marc Valée – Big Little Lies
George C. Wolfe – The Immortal Life of Henrietta Lacks
[Source: DGA]
Topics: Awards, Movies, News
Tags: Christopher Nolan, DGA, DGA Awards, Dunkirk, Get Out, Greta Gerwig, Guillermo del Toro, Jordan Peele, Lady Bird, Martin McDonagh, The Shape of Water, Three Billboards Outside Ebbing Missouri
CES 2018: No Boob Tube – Willow's All-In-One Wireless, Tubeless Breast Pump Is The Easiest Way To Feed Your Baby (Video)
Bryan Fuller Helping To Develop TV Series Based On Anne Rice's 'The Vampire Chronicles'
• Greta Gerwig and Noah Baumbach To Write Screenplay For 'Barbie' Film Starring Margot Robbie
• A24 To Screen Movies On US Billboards Including 'The Witch' In New Hampshire, 'Lady Bird' In California, and 'Moonlight' In Florida
• Movie Review: Toy Story 4
• Blu-ray Review: Us
• Official Trailer For 'Scary Stories To Tell In The Dark' Movie Released


The new issue of Woman magazine is out now!
In this week's issue of Woman magazine, we've got an EXCLUSIVE interview with Amanda Holden, Victoria Wood opens up about being unlucky in love, Helen Skelton shares some secrets with us, and find out what the stars of Downton are doing next.
Plus our favourite fashion pieces, the best bags for working days, and we bring you the Woman beauty awards – discover the perfect products to invest in today!
All this, as well as the diet to help you drop a dress size in 14 days, amazing supermarket offers from our food editor, delicious recipes ready for the BBQ this summer, great ideas for decorating your garden, and the full round up of what to do if you're planning on staying in or going out this weekend.
Pick up the new issue in shops, or download Woman magazine today via your tablet or iPad.


text:
Borrow books from PA libraries. Enter the 13-digits back of F&M ID to proceed.
Core Research Databases
Social Sciences Full Text This link opens in a new window
Indexes over 300 English-language periodicals covering anthropology, economics, political science, psychology, social work, sociology, and related fields. Includes both Abstracts and Full Text Articles. Covers 1983 to the present. This is the best place to begin because it includes the major scholarly journals in Political Science.
Clarivate Analytics' premier citation databases in the sciences (physical, life and social) and arts & humanities. Search for articles that cite a known author or work or by standard search queries. Covers 1864 to the present.
A collection of full-text articles from over 3000 scholarly journals, many dating from the nineteenth-century to the last 4 or 5 years. A growing number of titles are available through the current issues, as part of their "Current Scholarship Program". Select Advanced Search to strategically search amongst 45 disciplines. Choose PDF from within JStor to properly view and print articles. For convenience, JStor is now searchable on Facebook.
Nexis Uni features more than 15,000 news, business and legal sources from LexisNexis—including U.S. Supreme Court decisions dating back to 1790—with an intuitive interface that offers quick discovery across all content types, personalization features such as Alerts and saved searches and a collaborative workspace with shared folders and annotated documents.
New York Times (ProQuest Historical Newspapers) This link opens in a new window
The New York Times Historical Archive offers full page and article images with searchable full text back to the first issue. The collection includes digital reproductions providing access to every page from every available issue. Sept. 18, 1851- 4 Years Ago, full-text.
CQ Almanac This link opens in a new window
The CQ Almanac Online Edition brings together authoritative congressional reporting since the 79th Congress. The site pairs all of the content from the print volumes with electronic features that make navigating the content fast and easy. Coverage runs from 1945-2017.
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What are the benefits of doing joint practices with another team and what can you gage from that?
Do joint practices break up the monotony of training camp?
How much game work do you need in the preseason?
Could a joint practice be as valuable as a preseason game?
What do you think the first string defense showed from preseason Week One to Week Two?
What have you seen from Marshon Lattimore?
Do you believe Lattimore is a 'gamer'?


text:
\section{Models and Training Specifications}
All our models were trained on one {\em 24 GeForce GTX 1080 GPU}.

\paragraph{Weak-Premise Ranking}
We use the code of \cite{han:2020} to train our ranking model {\em Bert-ltr}, with BERT-Base, Uncased for embedding (12-layer, 768-hidden, 12-heads, 110M parameters). The model was trained for 100k steps, which took almost 9 hours of training.

\paragraph{Premise Attack Generation}
We use the transformers library to train our generation models, with GPT as a pre-training language model (12-layer, 768-hidden, 12-heads, 110M parameters. OpenAI GPT English model). The model was fine-tuned for six epochs, which took almost one day of training. In the generation time, we use top-$k$ sampling technique \cite{huang:2019}, with the following parameters: top-$k$=50, top-$p$=0.95, and temperature=1.0. We generate counters of a minimum of 100 tokens and a maximum of 150 tokens.

\paragraph{Evaluation Measures}
The BLEU and METEOR scores are computed using the NLTK library (\url{https://www.nltk.org/}). Significance tests were performed using the Scipy library (\url{https://www.scipy.org/}).

\section{Example Counters}
In this section, we present example counters generated by our approach and \newcite{hua:2019}

\input{table-example-generated-counters-full}
\section{Ethical Statement}
We acknowledge that ethical issues might arise from our work. First, we would like to ensure that we did not violate user privacy when using data from public platforms. By reusing pre-trained models, our approach might have inherited some forms of bias. Mitigating such bias is still ongoing research. It is worth noting that our experiments show that our approach is far from being ready to be used as an end technology. Our goal is to advance the research on this task.

\section*{Acknowledgments}

This work was partially supported by the German Research Foundation (DFG) within the Collaborative Research Center ``On-The-Fly Computing'' (SFB~901/3) under the project number~160364472.
\section{\@startsection {section}{1}{\z@}{-2.0ex plus -0.5ex minus -.2ex}{1ex plus 0.3ex minus .2ex}{\large\bf\raggedright}}

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\begin{document}
	\input{acl21-argument-undermining-pre}
	\input{acl21-argument-undermining-part1}
	\input{acl21-argument-undermining-part2}
	\input{acl21-argument-undermining-part3}
	\input{acl21-argument-undermining-part4}
	\input{acl21-argument-undermining-part5}
	\input{acl21-argument-undermining-summ}
	\input{acl21-argument-undermining-ethic}

\section{Introduction}

Following \newcite{walton:2009}, a counter-argument can be defined as an attack on a specific argument by arguing against either its claim (called {\em rebuttal}), the validity of reasoning of its premises toward its claim ({\em undercut}), or the validity of one of its premises ({\em undermining}). Not only the mining and retrieval of counter-arguments have been studied \cite{peldszus:2015,wachsmuth:2018a}, recent works also tackled the generation of counter-arguments. Among these, \newcite{bilu:2015} and \newcite{hidey:2019} studied the task of contrastive claim generation, the former in a partly rule-based manner, the latter data-driven. Moreover, \newcite{hua:2019} proposed a neural approach. So far, however, research focused only on rebutting a given argument, ignoring the other aforementioned types. We expand this research by studying to what extent argument undermining can be utilized in counter-argument generation.

\input{table-intro-example}

In argument undermining, the validity of some premises is questioned. Such a phenomenon can be observed often in online discussions on social media. For example, in the discussion excerpt in Table~\ref{table-intro-example}, taken from the Reddit forum {\em changemyview},%
\footnote{https://en.wikipedia.org/wiki/R/changemyview}
a user contests the whole stated argument (claim and premises) refers to the specific premise highlighted in the table (on Reddit, it is the quoted part of the text). This implies two steps: first, to identify a potentially weak and thus attackable premise in the argument, and second, to counter it.

\enlargethispage{\baselineskip}
In this work, we propose to tackle the task of counter-argument generation by attacking one of the weak premises of an argument. We hypothesize that identifying a weak premise is key to effective counter-argument generation---especially when the argument is of high complexity, comprising multiple interlinked claims and premises, making it hard to comprehend the argument as a single unit. Figure~\ref{approach.pdf} illustrates our two-step pipeline approach: it detects premises that may be attackable and then generates a counter-argument addressing one or more of these premises. To identify weak premises, we build on the work of \newcite{jo:2020}, who classify attackable sentences using BERT. In contrast, we rank premises based on their attackability concerning the argument's main claim, utilizing the learning-to-rank approach of \newcite{han:2020}. For the second step, similar to \newcite{wolf:2019}, we fine-tune a pre-trained transformer-based language model \cite{radford:2018}, in a multi-task learning setting: next-token classification and counter-argument classification.

\bsfigure{approach.pdf}{Argument undermining: Instead of countering a given argument directly, our approach first ranks the argument's premises by weakness. Then, an attack focused on the weakest premises is generated.}

In our experiments, we make use of the changemyview (CMV) dataset of \newcite{jo:2020}, where each instance is a post consisting of a title (say, an argument's claim) and a text (the argument's premises). Some of the sentences in the text are quoted by comments to the post. These sentences are considered to be weak/attackable premises\footnote{Our assumption is that each sentence represents a premise supporting the main claim mentioned in the title of the post}. We further extend the dataset by collecting texts from comments, defining counter-arguments.

To analyze our approach, we evaluate both of its steps individually as well as in combination. In particular, we first compare our ranking model for detecting attackable premises to \newcite{jo:2020}, observing significant improvements in the effectiveness. Second, given the ground-truth attackable premise (the quoted sentences), we evaluate our counter-argument generation model against several baselines. Our automatic evaluation provides evidence that training the model with the weak premise annotated significantly boosts the scores across all metrics. We additionally confirm these results by a manual evaluation, indicating that our approach is better than the baseline in 56\% of the cases. Finally, we apply our generation model based on the automatically detected weak premises and compare it to the approach of \newcite{hua:2019}, which generates counter-arguments with opposing stance to the argument (i.e., rebuttals). While the automatic evaluation here was not in favor of our approach, the manual evaluation gave evidence of the favorability of our approach on all three tested quality dimensions. To summarize, our contributions are\footnote{Code and resources can be found \url{https://github.com/webis-de/ACL-21}}:
\begin{itemize}
	\setlength{\itemsep}{0pt}
	\item
	A model for detecting premise attackability, achieving state-of-the-art effectiveness.
	\item
	A new approach to counter-argument generation that identifies and attacks weak premises.
	\item
	Empirical evidence of the importance of considering a specific attackable premise in the argument when generating a counter-argument.
\end{itemize}
\section{Related Work}

Recently, text generation has gained much interest in computational argumentation, both for single claims and complete arguments. \newcite{bilu:2015} composed opposing claims combining rules with classifiers, whereas \newcite{hidey:2019} tackled an analog task with neural methods. \newcite{alshomary:2020} reconstructed implicit claims from argument premises using triplet neural networks, and \newcite{gretz:2020} explored ability of GPT-2 to generate claims on topics. Recently, \newcite{alshomary:2021} studied how to encode specific beliefs into generated claims. \newcite{sato:2015} generated full arguments in a largely rule-based way. \newcite{el-baff:2019} modeled argument synthesis as a language modeling task, and \nopagebreak{\cite{schiller:2020}} studied the neural generation of arguments on a topic with controlled aspects and stance. Unlike all these, we deal with {\em counter}-arguments.

Research exists for mining attack relations \cite{cocarascu:2017,chakrabarty:2019,orbach:2020}, mining counter-considerations from text \cite{peldszus:2015}, and retrieving counter-arguments \cite{wachsmuth:2018a, orbach:2019}. However, only the consecutive works of \newcite{hua:2018,hua:2019} addressed the generation task. Their latest neural approach takes an argument or claim as input and generates a counter-argument rebutting it. Differently, we consider countering an argument by attacking one of its premises, known as {\em undermining} \cite{walton:2009}.

Part of our approach is to identify attackable premises, which can be studied from an argument quality perspective. That is, a premise is attackable when it lacks specific quality criteria. A significant body of research has studied argument quality assessment, with a comprehensive survey of quality criteria presented in \newcite{wachsmuth:2017a}. Implicitly, we target criteria such as a premise's acceptability or relevance. Still, we follow \newcite{jo:2020} in deriving attackability from the sentences of posts that users in the Reddit forum CMV attack. These sentences represent premises supporting the claim encoded in the post's title. The authors experimented with different features that potentially reflect weaknesses in the premises. Their best model for identifying attackable premises is a BERT-based classifier. We use their data to learn weak premise identification, but we address it as a learning-to-rank task.

As for text generation, significant advances have been made through fine-tuning large pre-trained language models \cite{solaiman:2019} on target tasks. We also benefit from this by utilizing a pre-trained transformer-based language model \cite{devlin:2018}, and we fine-tune it in a multi-task fashion similar to \newcite{wolf:2019}.
\section{Approach}
\label{sec:approach}

As sketched in Figure ~\ref{approach.pdf}, our pipeline approach counters an argument by attacking the validity of one of its potentially weak premises. This section presents the two main steps of our approach: first, the ranking of weak and thus attackable premises, and second, the generation of the weak premises' attack.

\subsection{Weak-Premise Ranking}

Given an argument in the form of a claim and a set of premises, the task is to identify the argument's attackable premises. Unlike previous work \cite{jo:2020}, we model the task as a ranking task instead of a classification task, in which, for each argument, we learn to rank its premises by their weakness relevant to the claim. Our hypothesis here is that the attackability of a premise can be better learned when considering both the claim and other premises of the argument.

We operationalize the weak-premise ranking similar to the ranking approach of \newcite{han:2020}. In particular, given a set of premises and the claim, we first represent each premise by concatenating its tokens with the claim's tokens, separated by special tokens {\em [cls]} and {\em [sep]}:

\begin{center}
	\em
	[cls] claim\_tokens [sep] premise\_tokens [sep]
\end{center}

\smallskip
Next, the resulting sequences are passed through a BERT model to obtain a vector representation for every premise. Each vector is then projected through a dense layer to get a score~$\hat{y}$ that reflects the weakness of the premise. Finally, a list-wise objective function (we use a Softmax loss) is optimized jointly on all premises of an argument as follows:
$$
l(y, \hat{y}) \;=\; - \sum_{i=1}^{n} y_i \cdot \log\Big(\frac{exp(\hat{y_i})}{\sum_{j=1}^{n} exp(\hat{y_{j}})}\Big),
$$

\noindent
where $y$ is a binary ground-truth label reflecting whether the given premise is attackable ($y=1$) or not ($y=0$). Given training data, we can thus learn to rank premises by weakness.

\subsection{Premise Attack Generation}

Given the ranking step's output, we identify the $k$ highest-ranked premises in an argument to be attackable (in our experiments, we test $k = 1$ and $k =3$). Then we generate a counter-argument putting the identified attackable premises into the focus. To this end, we follow \newcite{wolf:2019} in using transfer learning and fine-tune a pre-trained transformer-based generation model on our task.

\bsfigure{gpt-based-ca-gen.pdf}{Architecture of our approach: Given an argument, a weak premise, and a counter, three embedding representations are generated and fed to the transformer to obtain hidden states from which the language model and classification heads learn the {\em Next-token prediction} and {\em Counter-argument classification} tasks respectively}

In our fine-tuning process, the input is a sequence of tokens created from two segments, the argument and the counter-argument:%

\begin{center}
	\em
	[bos] arg\_tokens [counter] counter\_tokens [eos]
\end{center}

The final token embedding is then a result of concatenating three embeddings: word and positional embeddings learned in the pre-training process, as well as a token-type embedding learned in the fine-tuning process. Here, the token type reflects whether the token belongs to the argument in general, to a weak premise, or the counter-argument. Now, we train our model jointly on two tasks:
\begin{itemize}
	\setlength{\itemsep}{0pt}
	\item
	{\em Next-token prediction.} Given a sequence of tokens, predict the next one.
	\item
	{\em Counter-argument classification.} Given two concatenated segments, is the second a counter-argument to the first.
\end{itemize}

The first task is similar to the next-sentence prediction task introduced in \cite{devlin:2018}, which was shown to be beneficial for multiple representation-learning tasks.

Figure~\ref{gpt-based-ca-gen.pdf} shows the architecture of our generation model. For training, we augment a given set of training sequences~$D$ by adding distracting sequences, which we use, for each argument and its weak premise, a non-relevant text instead of the counter-argument. Given a sequence of tokens $d = (t_1, t_2, \cdots, t_n) \in D$, we then optimize the following two loss functions jointly with equal weighting:
\begin{eqnarray*}
	L_1(\Theta) & = & \sum_{d \in D} \sum_{t_i \in d}^{} \log P(t_i \,|\, t_{i-k}, \cdots,t_{i-1}; \Theta), \\
	L_2(\Theta) & = & \sum_{d_j \in D}^{} \log P(y_j \,|\, t_1, \cdots, t_n; \Theta),
\end{eqnarray*}

\noindent
where $\Theta$ denotes the weights of the model, $k$ is the number of previous tokens,  and $y_j$ is the ground-truth label of the sequence, indicating if the second segment of the sequence is a counter or not.
\section{Data}

As proposed, the presented approach models the task of counter-argument generation as an attack on a potentially attackable premise. Such behavior is widely observed on the Reddit forum {\em changemyview (CMV)}. In particular, a user writes a new {\em post} that presents reasons supporting the pro or con stance towards a given topic (captured in the {\em title} of the post), asking the CMV community to challenge the presented view. In turn, other users quote specific segments of the post (usually a few sentences) and seek to counter them in their {\em comment}. An example has already been given in Figure \ref{table-intro-example}.

The structure induced by CMV defines a suitable data source for our study. Specifically, we create the following distantly supervised mapping:
\begin{itemize}
	\setlength{\itemsep}{0pt}
	\item
	The title of the post denotes the {\em claim} of the user's argument;
	\item
	the text of the post denotes the concatenated set of the argument's {\em premises};
	\item
	the quoted sentence(s) denote the {\em attackable (weak) premises}; and
	\item
	the quoting sentences from the comment denote the {\em counter-argument}.
\end{itemize}

In our work, we build on the CMV dataset of \newcite{jo:2020}, where each instance contains a post, a title, and a set of attackable sentences (those quoted in the comments). We use the same split as the authors, consisting of 25.8k posts for training, 8.7k for validation, and 8.5k for testing. We extend their dataset by further collecting the quoting sentences from the comments (i.e., the counter-arguments). The final dataset compiles 111.9k triples of argument (claim and premises), weak premise (one sentence or more), and a counter-argument (a set of sentences), split into 67.6k training, 23k validation, and 22.3k for testing instances.
\section{Evaluation}
\label{sec:evaluation}

In the following, we present the experiments we carried out to evaluate both steps of our approach individually as well as in a pipelined approach. On the one hand, we aim to assess the applicability of identifying weak premises in an argument and the impact of targeting them in the process of counter-argument generation. On the other hand, our goal is to assess how well counter-argument generation via {\em undermining} is compared to other known counter-argument generation approaches.

\subsection{Weak-Premise Ranking}

As presented, we approached the task of finding attackable premises by learning to rank premises by their weakness with respect to the main claim.

\paragraph{Approach}

Based on the code of \newcite{han:2020} available in the Tensorflow learn-to-rank framework \cite{pasumarthi:2019}, we used a list-wise optimization technique that considers the order of all premises in the same argument.%
\footnote{We also experimented with point-wise and pairwise techniques, but list-wise was best. }
We trained our ranking approach on the CMV dataset's training split and referred to it as {\em bert-ltr} below.\footnote{Training details can be found in the appendix.}

\paragraph{Baselines}

We compare our approach to the Bert-based classifier introduced by \newcite{jo:2020}, trained on the same training split using the authors' code. We use their trained model to score each premise and then rank all premises in an argument accordingly. We call this {\em bert-classifier}. As \newcite{jo:2020}, we also consider a {\em random baseline} as well as a baseline that ranks premises based on {\em sentence length}.

\paragraph{Measures}

To assess the effectiveness, we followed \newcite{jo:2020} in computing the precision of putting a weak premise in the first rank ($P@1$), as well as the accuracy of having at least a weak premise ranked in the top three ($A@3$).

\paragraph{Results}

Table \ref{weak-premise-eval-table} shows the weak-premise ranking results. We managed to almost exactly reproduce the values of \newcite{jo:2020} for all three baselines. Our approach, {\em bert-ltr}, achieves the best scores according to both measures. In terms of a one-tailed dependent student-$t$ test, the differences between {\em bert-ltr} and {\em bert-classifier} results are significant with at least 95\% confidence. These results support our hypothesis of the importance of tackling the task as a ranking task with respect to the main claim. Below, we will use our weak-premise ranking model in the overall approach, i.e., to automatically select attackable premises in an argument.

\input{table-weak-premise-automatic-eval.tex}

\subsection{Premise Attack Generation}
\label{eval-weak-premise-ranking}

Next, we evaluate our hypothesis of the importance of identifying weak premises in the process of counter-argument generation. To focus on this step, we use the ground-truth weak premises in our data. These are the quoted sentences in the post, considered potentially attackable premises.

\paragraph{Approach}

We used OpenAI GPT as a pre-trained language model. We trained two versions of our generation model: {\em our-model-w/} with an extra special token (\textit{[weak]}), surrounding the attackable sentences to give an extra signal to our model, and once {\em our-model-w/o} without it. We fine-tuned both versions with the same settings using the transformers library \cite{wolf:2020} for six epochs\footnote{We stopped at six epochs because we observed no gain in terms of validation loss}. We left all other hyperparameters to their default values.  As mentioned, the model's input is a sequence of tokens constructed from the argument (with weak premises highlighted) and either the correct counter or a distracting sequence. We randomly select one sentence from the original post to be the distracting sequence for each input instance.

\input{table-counter-gen-eval}

\paragraph{Baseline}

We compare our model to a GPT-based model fine-tuned on a sequence of tokens representing a pair of an argument (title and post) and a counter-argument. We consider this as a general counter-argument generation model, trained without any consideration of weak premises. We train the baseline using the same setting as our model. We refer to it as {\em counter-baseline}.

\paragraph{Automatic Evaluation}

To assess the importance of selecting attackable sentences, we evaluated the effectiveness of our model in different inference settings in terms of what is being attacked:
(1)~the {\em claim} of the argument,
(2)~a {\em random premise}, or
(3)~a {\em weak premise} given in the ground-truth data.
In the random setting, we randomly selected three premises from the argument, and we generated one counter for each. The final result is the average of the results for each.

\bsfigure{overlap-with-weak-premise.pdf}{Premise attack generation: Mean token overlap between the ground-truth weak premises and the counters generated by each evaluated approach.}

We computed METEOR and BLEU scores, comparing the generated premises to (a)~the exact counter sentences of the quoted weak premise and (b)~the full argument. We performed this automatic evaluation on 1k posts from the test split.

\paragraph{Results}

As shown in Table \ref{counter-gen-eval-table}, the best results are achieved by {\em our-model-w/o} in all cases when identifying the weak premises in the input. Encoding the knowledge about weak premises as token types is sufficient, and adding an extra special token doesn't help. Although the differences between our best model and the baseline are not big, they are significant according to the one-tailed dependent student-$t$ test with a confidence level of 95\%.
For both versions of our model, best scores are achieved when considering the weak premises as the target (except for the first METEOR column). However, not all these differences are significant. This gives evidence that exploiting information about weak premises in the training of counter-argument generation approaches can improve their effectiveness.

To further assess the relationship between the generated counters and the attacked premises, we computed the proportion of covered content tokens in the weak premise for the two versions of our model and the baseline. Figure \ref{overlap-with-weak-premise.ai} shows a histogram of the percentages. Clearly, both versions of our model have higher coverage of the annotated weak premise than the baseline.

\input{table-manual-eval-1}

\paragraph{Manual Evaluation}

To analyze the generated counter-arguments more thoroughly, we carried a manual evaluation study on a sample of 50 random examples. Two authors of the paper inspected the sample comparing the two versions of our model. The results were in favor of {\em our-model-w/o}. Therefore, we compared only {\em our-model-w/o} against the {\em counter-baseline}. In particular, we assessed the relevance and appropriateness of the output of the two for each example. Given an argument, the highlighted premise to be attacked, and the two counters, we asked three annotators who hold an academic degree and are fluent in English (no author of this paper) to answer two questions:
\begin{enumerate}
	\setlength{\itemsep}{0pt}
	\item
	Which text is more relevant to the highlighted premise?
	\item
	Which text is more appropriate for being used as a counter-argument?
\end{enumerate}

\paragraph{Results}

As shown in Table \ref{manual-eval-1-table}, considering the majority vote, annotators favored our model in 56\% of the cases in both tasks. These results give further evidence supporting our hypothesis of the importance of identifying weak premises. Considering the given task as a ranking task, we used Kendall's $\tau$ to compute the annotator's agreement. The mean pairwise agreement was  0.41 for the relevance assessment and 0.23 for appropriateness. Clearly, assessing the text's appropriateness of being a counter-argument is more subjective and more challenging to judge than the relevance task.

\subsection{Overall Approach}

Finally, we assess the overall effectiveness of our counter-generation approach, that is, when we identify weak premises automatically using {\em Bert-ltr} and then generating a counter-argument using  {\em our-model-w/o},  focusing on the selected weak premises.

\paragraph{Approach}

Due to the limited {\em P@1} value of our ranking model (see Table \ref{weak-premise-eval-table}), we evaluate two variations of our overall approach that differ in terms of what premises to attack. The first variant attacks the weakest premise. In the second, we first generate three counters considering each of the top three weak premises. Then, we select the counter that has the most content-token overlap with the corresponding weak premise.

\input{table-overall-automatic-eval}

\paragraph{Baselines}

On the one hand, we compare our approach to the {\em counter-baseline} from the previous section. On the other hand, we consider the state-of-the-art counter-argument generation approach of \newcite{hua:2019}, an LSTM-based Seq2seq model with two decoders, one for selecting talking points (phrases) and the other for generating the counter given the selection.

\paragraph{Automatic Evaluation}

While the approach of \newcite{hua:2019} learns from a dataset collected from the same source (CMV), it requires retrieving relevant argumentative texts with a stance opposite to the input argument. Due to the complexity of the data preparation, we decided instead to evaluate all approaches on the test split of \newcite{hua:2019}.%
\footnote{We verified that all posts in their test split do not appear in our training split.}
As a result,  \newcite{hua:2019}'s approach is trained on their training split, while our approach is trained on our training split, and then both are evaluated on the same test split of \newcite{hua:2019}. This can be considered a somewhat unfair setting for our approach due to certain domain differences since the dataset of \newcite{hua:2019} comprises political topics only.
Similar to Section~\ref{eval-weak-premise-ranking}, we generated counters for 1k examples and computed METEOR and BLEU scores of the generated counters with respect to the ground-truth counters, which are here full arguments (CMV comments).

\paragraph{Results}

Table \ref{overall-automatic-eval-table} shows that our approach outperforms the counter-baseline in both settings, even with weak premises selected automatically. Considering the top-3 weak premises instead of the top-1 improves the results. The best scores are achieved by {\em Hua and Wang}, though. A reason for this may be the slight domain difference between our model's training data and the test data used for evaluation. Another observation is that the scores of both our approach and the baseline increase compared to Table~\ref{counter-gen-eval-table}. This is likely to be caused by the higher number of ground-truth references for each instance in data of \newcite{hua:2019} compared to the test split of our data, making it more likely to have token overlaps.

\paragraph{Manual Evaluation}

Given the known limited reliability of automatic generation evaluation, we conducted another user study to evaluate the quality of the generated counters by our model and {\em Hua and Wang}. We evaluate the same quality dimensions used previously by \newcite{hua:2019}:
\begin{itemize}
	\setlength{\itemsep}{0pt}
	\item
	{\em Content Richness.} The diversity of aspects covered by a counter-argument.
	\item
	{\em Correctness.} The relevance of a counter-argument to the given argument and their degree of disagreement.
	\item
	{\em Grammaticality.} The grammatical correctness and fluency of a counter-argument.
\end{itemize}

We used the Upwork crowd working platform to recruit three annotators with English proficiency and experience in editorial work.%
\footnote{Upwork, \url{http://upwork.com}}
We asked each of them to evaluate a sample of 100 examples. Each contained an argument (claim and premises) and two counters (one of each approach). We asked the annotators to compare the counters and assess each with a score from 1 (worst) to 5~(best) for each quality dimension.

\input{table-final-manual-evaluation}

\paragraph{Results}

The results are presented in Table \ref{table-final-evaluation}. Unlike in the automatic evaluation, the annotators gave, on average, higher scores on all quality dimensions to our generated counters than to those of {\em Hua and Wang}.%
\footnote{We note that the scores of Hua and Wang in Table \ref{table-final-evaluation} are notably lower than those reported by \newcite{hua:2019}. We believe this to be due to the comparison with our approach that affected the annotator's scores.}
Bringing knowledge from pre-trained language models (GPT) generally seems to contribute to the {\em grammaticality} and the {\em richness} of the generated counters. In terms of generating a {\em correct} counter, focusing the generation model on a specific weak premise in an argument seems to help (2.65 vs.\ 1.81), even though the results are far from perfect. Manual inspection revealed that far from all generated arguments are counters to exactly what is in the argument, indicating more room to work on this topic.

\input{table-example-generated-counters}

Krippendorff's $\alpha$ values show that the annotators had a fair agreement on {\em grammaticallity} and {\em correctness} tasks (given the subjectiveness of the tasks), but only slight agreement on {\em content richness}. We, therefore, think that the results for the latter should not be overinterpreted.

In Figure \ref{table-example-generated-counters}, we show an example argument in favor of {\em income inequality}. Our approach considers the premise ``being poor does not entitle someone to the cash of the rich people''. It then generates a counter-argument on the topic of inequality, focusing on the fact that ``being poor limits the ability to contribute to society". In contrast, the counter-argument generated by {\em Hua and Wang} diverges to address ``low-income housing'' which is less relevant to the topic. More examples of generated counters can be found in Figure~5 in the appendix.

\section{Conclusion}

In this work, we have proposed a new approach to counter-argument generation. The approach focuses on argument {\em undermining} rather than {\em rebuttal}, aiming to expand the research in this area. The underlying hypothesis is that identifying weak premises in an argument is essential for effective countering.
To account for this hypothesis, our approach first ranks the argument's premises by weakness and then generates a counter-argument to attack the weakest ones.

In our experiments, we have first evaluated each step individually. We have observed state-of-the-art results in the weak-premise identification task. Our results also support the need for identifying weak premises to generate better attacks. We have also evaluated the overall approach against the state-of-the-art approach of \newcite{hua:2019}. While we did not beat that approach in automatic evaluation scores, independent annotators favored the counter-arguments generated by our approach across all evaluated quality dimensions.

We conclude that our approach improves the state of the art in counter-argument generation in different respects, providing support for our hypothesis. Still, the limited manual evaluation scores imply notable room for improvement. Most importantly, controlling the stance of the generated counters is yet to be fully solved.

redpajama_set_name: RedPajamaArXiv


text:
Illinois Lawmakers Eye State-Level Child Tax Credit
March 18, 2022 Topic: Tax Credit Blog Brand: Politics Tags: U.S. PoliticsU.S. EconomyChild Tax CreditInflationGovernment Benefits
Supporters claim the bill would make 1.2 million more people in the state eligible for the earned income credit.
by Stephen Silver
The expanded child tax credit—part of the American Rescue Plan Act—led to a larger tax credit and direct payments for American families for the last six months of 2021. The credit, however, expired at the end of 2021, and last-ditch efforts to expand it by another year failed in the Senate in late 2021. As a result, families this year have had to make do without those monthly checks.
Just this year, several states have passed or considered passing their own child tax credits, including Alabama and New Mexico. The latest state trying to do something similar is Illinois, the Center Square reported.
A pair of bills in the Illinois legislature would serve to expand the earned income credit (EIC) and create a state-level child tax credit (CTC). The state does not currently offer its own child tax credit. "This has been proven to affect poverty," said Rep. Carol Ammons, a sponsor of HB4920. "This is an anti-poverty policy that we know works and gives people direct relief."
According to the politicians, the legislation would make 1.2 million more people in the state eligible for the earned income credit and provide more financial support to 3.6 million people.
"It really is putting money back in workers' pockets, so it is boosting their earnings so that they are able to spend that in the economy," said Allison Flanagan, associate director of Budget and Policy for the Center for Tax and Budget Accountability, said in the announcement. "You get sort of this duplicative effect. It is helping the household and those households are going and spending the money."
A letter to the editor of the Chicago Sun-Times this week showed how people in Illinois are struggling without the federal child tax credit payments. "Families like mine were fortunate that the child tax credit was distributed monthly last year, to offset rising prices. However, the end of these payments in January disrupted the finances of families everywhere," Colyn Belknap wrote in the letter. "The disruption could easily be solved by legislators if they're willing to compromise on the credit."
Columbia's Center on Poverty and Social Policy issued a report last month showing that 3.7 million more children were in poverty in January of this year, the first month after the expiration of the expanded child tax credit. That metric had dropped by a similar amount during the first month that the credit was in place last year.
Stephen Silver, a technology writer for The National Interest, is a journalist, essayist and film critic, who is also a contributor to The Philadelphia Inquirer, Philly Voice, Philadelphia Weekly, the Jewish Telegraphic Agency, Living Life Fearless, Backstage magazine, Broad Street Review and Splice Today. The co-founder of the Philadelphia Film Critics Circle, Stephen lives in suburban Philadelphia with his wife and two sons. Follow him on Twitter at @StephenSilver.


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text:
San Giorgio e il drago è un dipinto a tempera e oro su tavola (62,2x38,8 cm) di Paolo Uccello, databile al 1423-1425 circa e conservato nella National Gallery of Victoria di Melbourne.

Storia
L'opera è nota da poco prima del 1860, quando passò in asta Christie's a Londra. Dalla collezione Davenport Bromley (1863) passò poi a quella di James Carnegie (dal 1867), sotto la quale fu esposta in deposito alla National Gallery of Scotland di Edimburgo dal 1922. Fu venduta poi al museo australiano nel 1949.

Il primo a pubblicare la tavoletta fu Raimond van Marle nel 1927, che la ascrisse al senese Domenico di Bartolo, ma già un anno dopo Roberto Longhi la collocava nell'ambito di Paolo Uccello, per poi recepirla solo quarant'anni dopo (nel 1968) nel catalogo autografo del pittore stesso. Carlo Volpe avanzò una datazione al 1430, anticipata poi dal Boskovits al periodo anteriore del 1425. Questa datazione si sposa bene anche coi motivi stilistici e con la composizione serrata, che sembra ispirarsi ai modelli serrati delle formelle della porta nord del Battistero di Firenze di Lorenzo Ghiberti, opera alla quale partecipò anche Paolo Uccello in qualità di aiuto di bottega.

Descrizione e stile
Paolo Uccello si cimentò sul tema del San Giorgio almeno altre due volte, in opere oggi alla National Gallery di Londra e al Museo Jacquemart-André di Parigi. Ma se in tali opere le scene sono calate in spazi ariosi e scalati in profondità tramite la prospettiva, in questo caso la battaglia tra il santo e il drago si svolge tutta in primo piano e, secondo un'iconografia assai rara, corpo a corpo. Il santo infatti è già sceso da cavallo ed ha già spezzato la sua lancia, cercando di colpire col mozzicone il drago che lo afferra in vita, con gli artigli e con le spire della coda. In basso la spada giace caduta a terra. Assistono a destra la principessa inginocchiata e il cavallo furente, che si piega ritrosamente come in un episodio della predella di Quarate. Sullo sfondo una città murata di pieno gusto gotico, intonata su fiabeschi toni azzurri e rosa, è sormontata da una grande apparizione dell'Eterno benedicente, in una raggiera incisa sul fondo oro.

Salta all'occhio l'assenza di un interesse prospettico con gli edifici incisi a mano libera sulla preparazione, o nel mancato scorcio della spada abbandonata, sebbene invece la lancia sia invece posata in posizione ordinatamente orizzontale. Di sapore ancora medievale, e quindi compatibili solo con una datazione piuttosto precoce, è poi l'uso estensivo delle lacche, verdi su ali e corpo del drago, nerastre sull'armatura argentata del santo, rosse sulla tiara di Dio Padre. Tocchi d'oro ravvivano alcuni dettagli della parte inferiore, come nelle ali del drago o nell'aureola del santo, dall'inconsueta forma a raggiera, o ancora nei gioielli della principessa e nei finimenti della bardatura del cavallo.

I colori chiari e luminosi, dalle tonalità astratte, venivano in quegli anni ripresi e sviluppati rispetto ai precedenti gotici dalle prime opere di Beato Angelico, con una preferenza per le tonalità azzurre e rosa riscontrabile anche nell'unica opera attribuita con relativa sicurezza alla fase giovanile di Paolo Uccello, l'Annunciazione di Oxford. In tale opera compaiono anche alcune aureole a raggiera, simili a quella del Dio Padre, e la stessa figura dell'Eterno è del medesimo tipo fisico, con barba e capelli voluminosi e indossante un triregno. Le rocce scheggiate, motivo di origine bizantina popolare per tutto il medioevo, si piegano qui in maniera ormai più morbida, creando scaglioni che pure si ritrovano nella stessa Annunciazione.

Note

Bibliografia
 Andrea De Marchi e Cristina Gnoni Mavarelli (a cura di), Da Donatello a Lippi, Officina pratese, catalogo della mostra, Skira, Milano 2013. ISBN 978-88-572-2039-0

Altri progetti

Collegamenti esterni

Dipinti di Paolo Uccello
Dipinti su san Giorgio
Dipinti nella National Gallery of Victoria
Dipinti su Dio Padre

redpajama_set_name: RedPajamaWikipedia


It's been an historic year for one of the countries we work in this year. Their country has been transformed in dramatic ways and Asian Access had the privilege of taking a small part in this sea changing season. The country was a former religious kingdom overthrown by a local insurgency which struggled to run the country. The religious groups dominating the nation were constantly fighting back, demonstrating in the capital city, causing all sorts of mayhem.
Nearly 13 years ago, Asian Access began building capacity of local pastors in the country and little did we know the impact that investment would make. The ongoing news demonstrates the power of our third core value as a mission: Accelerating Kingdom Leaders.
While investing deeply in the lives of 12-15 pastors every 2-3 years, an emerging vision was birthed through their communal prayer and learning. They set a vision to plant 75,000 churches in their nation and the results have been astounding. It became a people movement that only God can take credit for. Beyond this vision, these pastors decided to tackle the issues of orphan care and anti-trafficking to solve some of societies greatest problems.
The small spark plug of investing in these pastors led to remarkable societal transformation. Several years ago, the then Maoist government of their nation, approached them asking for help in dealing with the radical religious groups. They said, "Can you help us write the first constitution of our nation by drafting the religious liberty sections?" By God's grace, that constitution was ratified this past summer! While it's an imperfect document due to many political twists and turns, it nevertheless laid the groundwork for future work toward religious liberty in their nation.
Imagine that! By just investing in a few key people, you can see entire nations changed. That's what Jesus' did and he implores us to go and do the same.
How might you invest your life to make a difference in your community, your neighborhood, or your nation? Walk with God and see where He might take you. Step in with a 'vibrant learning community' and see how God inspires you collectively to make a difference. You never know where God might take you!