Evaluation Method of Public Physical Training Quality Based on Global Topology Optimization Deep Learning Model

In the quality evaluation of public sports training, the selected indicators are not comprehensive, resulting in some errors in the results of quality evaluation. Therefore, this paper designs a public sports training quality evaluation method based on the deep learning model of global topology optimization. Determine the basic principles of public sports training quality evaluation, determine the human coordinate points of public sports training by determining the basic section and basic axis of human training, and extract the data of public sports training quality evaluation. On this basis, quantify the public sports training quality evaluation index, construct the evaluation matrix, calculate the weight of the evaluation index, and determine the importance of the public sports training quality evaluation index. Preprocess the public sports training quality evaluation index set, search the optimal fitness value in the global topology optimization depth learning model, introduce the global topology optimization depth learning model, input the evaluation index and output the evaluation quality results, and realize the quality evaluation of public sports training. The experimental results show that the evaluation method in this paper can improve the accuracy of the evaluation results and is feasible.

Introduction
With the rapid development of the social economy, people's living standards are also improving, but people's physical fitness has been declining year by year. e emergence of this phenomenon makes people feel very surprising but also caused widespread concern from all walks of life. An important reason for the decline of people's physical quality is the lack of physical exercise [1]. Obesity, weakness, and other problems one after another become major obstacles to health. In the long run, the physical condition of the masses will eventually become an important issue affecting the development of the national physical condition. In order to promote the healthy development of the human body and improve the level of physical health, a large number of human and material resources are invested to organize and implement physical health tests [2]. However, in reality, these efforts have not achieved the expected effect, and instead, the resulting evaluation based on "physical health level" leads to social cognition deviation, false reported data of physical health tests, and many other problems [3]. erefore, it is urgent to enrich and perfect the evaluation index system of mass sports fitness in China, whether from the needs of the times of sports fitness or from the needs of the country to promote the rapid development of sports fitness. e quality assessment of public sports training is to test the quality effect of public sports and is also an effective way to regulate the process of sports training [4]. It conducts a comprehensive and effective evaluation of the quality and process of public sports training, finds out problems in the process of public sports training, and promotes the further development of public sports training [5]. e significance of teacher training quality evaluation in traditional schools lies in the investigation of the basic situation and effectiveness of training work and the summary of experience through evaluation, so as to further improve training work and improve training effectiveness [6]. erefore, it is of great significance to evaluate the quality of public physical training. erefore, relevant researchers have conducted a lot of research and achieved some results. Reference [7] proposed a method of physical education quality evaluation based on integration.
rough the methods of literature research, interview, and field investigation, this paper explores the current evaluation of physical education quality, focuses on the analysis of theoretical research and practical needs, repositions from an integrated perspective, constructs a multiclassification system of physical education quality, and focuses on the real content of school physical education quality evaluation. e evaluation of physical education academic quality focuses on knowledge, ability, action, and health and systematically studies the respective evaluation points and the key points that should be grasped in the systematic setting of physical education quality evaluation standard. However, this method is more theoretical than practical. Reference [8] proposed an evaluation method of public sports service supply level from the perspective of basic public service equalization. e method used the entropy method to evaluate the single factor supply level and the comprehensive level of public sports service supply and used the exploratory spatial data analysis method to analyze the spatial distribution characteristics of the comprehensive level and single factor supply level of public sports service in China.
e results show that this method can effectively evaluate public service facilities, but it cannot directly evaluate the quality of public sports training, and the results obtained are not practical. Reference [9] proposed an IPA-BASED public sports service quality evaluation model research. is method uses mathematical statistics and logical analysis method and IPA analysis method to design the evaluation model of public sports service quality and takes a certain national fitness center as the empirical object to test, in order to explore the path for the continuous improvement of public sports service quality in China. In this study, the public sports service quality evaluation scale covers five dimensions: availability, facility quality, health status, staff quality, and cost performance, with a total of 30 evaluation indicators. A total of 343 valid questionnaires were collected through the empirical test, which were reasonable in reliability and validity and suitable for quality evaluation. According to the coordinate region where the index evaluation results are located, the method can find problems and put forward improvement countermeasures for the national fitness center. IPA analysis method is suitable for the practical needs of public sports service quality evaluation in China and can be popularized and applied in a wider range. However, it needs to be improved by itself and further promoted with the integration of other quality management tools. Reference [10] proposed a public sports service quality evaluation method for large stadiums based on public perception. Based on the perspective of public perception and the modified SERVQUAL model, this method constructed the evaluation index system of public sports service quality in large stadiums and gymnasiums. Taking 2,685 valid questionnaires as sample data, the empirical research on the public sports service quality of 5 large stadiums in Hubei province is carried out by using AHP, fuzzy comprehensive evaluation, and TOPSIS method. However, this method has few indicators for sports training, so it is difficult to get accurate evaluation results.
Aiming at the problems in the above methods, this paper designs a new public sports training quality evaluation method based on the global topology optimization deep learning model. By determining the basic criteria for the quality evaluation of public sports training, the effectiveness of evaluation is improved; the quality data of public sports training is extracted; the quality evaluation indicators of public sports training are constructed; the indicators are normalized; and the quality evaluation of public sports training is realized by using the global topology optimization deep learning model. e results show that the proposed method can effectively improve the accuracy of the evaluation results and has certain feasibility.

Evaluation Principles and Index System
Construction of Public Sports Training Quality e principle of subjectivity means that the responsible subject must be clear in the quality evaluation of public sports training. Students are the main body of public sports training quality evaluation. In the process of evaluation, we should enhance the consciousness of the quality subject. e construction of the evaluation system should fully consider the main role of students, establish and improve the quality evaluation system, and improve the evaluation quality. Give play to the main role and continue to improve the quality of public sports training.

Objective Principle.
e goal principle means that the quality system of public sports training should be consistent with the training purpose. e ultimate goal is to improve the quality of public sports training [11]. e objective principle requires the evaluation to pay attention to the establishment, guarantee, and implementation of project objectives.

Systematic Principle.
e systematic principle refers to that all parts of the quality evaluation of public sports training are interrelated and interactive. To give full play to the overall advantages of evaluation, we must proceed from the whole and deal with it comprehensively. e part is the constituent element of the whole. e part should obey the whole. Any decision should not violate the overall goal of strategic decision-making.

Scientific Principle.
e scientific principle refers to the construction of public sports training quality evaluation system to select the most characteristic and representative influence factors, select the most widely used index and the best response system on the basis of scientific research, optimize it to the simplest and best, and form the most scientific index system [12].

Principle of Feasibility.
e principle of feasibility means that the scheme chosen in the construction of public sports training quality evaluation system cannot exceed the subjective and objective conditions. First of all, the evaluation index can use the corresponding sentences to explain its contents, get the corresponding data through the corresponding measurement tools and draw a clear conclusion, and use the principle of quantitative index measurement as the main and qualitative index measurement as the auxiliary. erefore, based on the scientific demonstration of various possible schemes, we should pursue the simplicity of scheme implementation.

Data Extraction of Public Sports Training Quality
Evaluation. In order to realize the effective evaluation of public sports training quality, it is necessary to clarify the relevant data of public sports training quality evaluation, that is, it is necessary to extract the data on human public sports training quality [13]. In order to facilitate the description and analysis of public sports, the basic plane and basic axis of the human body are usually defined in the standard upright position, as shown in Figure 1.
e basic plane of the human body includes sagittal, coronal, and horizontal planes, which are perpendicular to each other. Among them, the sagittal plane is a vertical section in the fore-and-aft direction, which divides the body into left and right parts along the fore-and-aft direction of the human body; coronal plane, also known as the frontal plane, is a vertical section in the left and right directions and divides the body into front and back parts along the left and right directions of the human body; and horizontal plane, also known as cross-section, is parallel to the ground and divides the body into upper and lower parts. e basic axes of the human body include sagittal, coronal, and vertical axes, which are perpendicular to each other [14]. Taking different basic axes of the human body as the tangent point, the relevant data on public sports training quality are determined. In human training data extraction, the relevant motion information data is extracted with the help of a camera. First, the five-parameter model of a linear camera is determined as follows: e matrix p i is the internal parameter matrix under the linear model of the camera, making two sets of translation movements in three-dimensional space and controlling the attitude of the camera for self-calibration; (f x , f y ) is the magnification coefficient of the image plane in the X-axis direction and the Y-axis direction: (u 0 , v 0 ). Main point coordinate: f s is the coupling amplification coefficient when the X-axis and the Y-axis are not perpendicular; e nonlinear model formula of the camera for extracting human training data is then determined as follows: where (a, b) is the human training image point coordinate under the linear model, (a, b) is the actual human training image point coordinate, and (D a , D b ) represents the nonlinear distortion value. e nonlinear model camera imaging of the same target training point at multiple different human training viewpoints is set to S (0) , S (1) , . . . , S (n) , and the feature matching point in the extracted human training is set to According to the determined characteristic matching points in human training, the training movement coordinates of different points in human training are determined as follows: Finally, matching human training points in public sports training and extracting public sports training quality assessment data requires a series of processing of images acquired by two cameras set side by side, and obtaining depth information, which has very important applications in robot navigation, three-dimensional reconstruction, and other fields. e stereo matching assumes that two cameras are identical and the optical axes are parallel with only lateral displacement between cameras. In the paired images, the pole line is parallel to the x-axis, and and horizontal state is maintained [15]. Since a completely ideal stereo camera configuration is difficult to achieve, feature detection, description, and sparse feature matching are required, that is, stereo correction to meet the assumed conditions of stereo matching. Here, feature detection is used to identify more obvious visual features that recur in multiple views. Such sparse features require a corresponding feature description for matching so that feature detection allows images from cameras with different viewpoints and different parameters. Get where E(p, q) is human training points in public sports training, c represents the weight value of stereo matching, and g represents the similarity value of sports training data.
In the process of extracting public sports training quality evaluation data, the basic section and basic axis of human training are determined, the human coordinate points of public sports training are determined through the five parameter model, and the evaluation of public sports training quality evaluation data is completed through three-dimensional matching and coefficient characteristics. Based on the relevant data of evaluation, it is convenient to carry out subsequent research.

Construction of Public Sports Training Quality Evaluation System,
at Is, Quantitative Research on Indicators. Based on the data extraction of public sports training quality evaluation, this paper takes these data as the research object to construct indicators and quantify them. First, build a public sports training quality evaluation system. e establishment of public sports training quality evaluation index system needs to be based on the guiding ideology and working principles of the evaluation work. In addition to considering the main characteristics of the training project, it should also be combined with the project characteristics and various qualities and abilities that teachers should have [16]. is paper classifies and analyzes the factors affecting the public sports training quality evaluation system and the professional characteristics of teachers and, combined with expert interview opinions, divides the factors affecting the public sports training quality into four major aspects, namely the first-class indicators of the evaluation system: training resources, training scheme, training process, and training effect. See Figure 2 for details.
For the evaluation of public sports training quality, there are many influencing factors. erefore, in the evaluation of public sports training quality, it has a certain fuzzy attribute, which is a more important part of the evaluation results. erefore, this paper will introduce the combination of the fuzzy comprehensive evaluation method and the deep data mining method to determine the membership of the evaluation index of public sports training quality [17].
To quantify the evaluation indexes of public sports training quality through the fuzzy comprehensive evaluation method, it is necessary to construct the influencing factor set, evaluation set, and membership degree of relevant influencing factors of public sports training quality evaluation indexes. e influencing factor set of evaluation indexes is as follows.
en, we construct the membership subset of the public sports training quality evaluation indicators, namely where L i , V i represents the specific membership set corresponding to the first index (or the teaching performance of a certain subject) corresponding to the related evaluation index of public sports training quality. At this time, the membership calculation formula of the specific public sports training quality evaluation index is as follows: where the value range of the lower angle mark is 1,2, . . .. P i is used for the specific index level and q i is used to represent the total number of participants participating in the public sports training quality evaluation.
After the scalarization of the above public sports training quality evaluation index, the weight calculation of the evaluation index is needed. e evaluation matrix is represented as W. e above evaluation indexes all have a subset of evaluation membership; all influencing factors are set as one; and the evaluation matrix of the evaluation index is constructed in the feasible domain of influence factors [18]. e membership value of the evaluation index of public sports training quality is obtained by normalization processing [19]. e calculation process is as follows: In order to improve the accuracy of the public sports training quality evaluation index, the membership degree and the evaluation membership degree value are calculated repeatedly to obtain the evaluation value of the impact index. e quantitative evaluation index of public sports training quality is obtained, and the fuzzy comprehensive evaluation results are as follows: 4 Journal of Environmental and Public Health At the same time, the effectiveness of the evaluation is improved according to this result. e quantitative process of public sports training quality evaluation indicators is shown in Figure 3: In the quantification of public sports training quality evaluation indicators, the public sports training quality evaluation indicators are quantified by the combination of a fuzzy comprehensive evaluation method and deep data mining method. On this basis, the evaluation matrix is constructed to calculate the weight of evaluation indicators and determine the importance of public sports training quality evaluation indicators, so as to lay a foundation for follow-up research.

Implementation of Public Sports Training Quality Evaluation Based on Global Topology Optimization and Deep Learning Model
Public physical training quality evaluation is the measurement, analysis, and evaluation of the quality of physical education teaching. It takes the process and results of the organic combination of teaching objectives, teaching contents, teaching methods, and other factors involved in teaching activities as the evaluation object. It is the evaluation of the overall function of teaching activities. It is assumed that the evaluation of public physical education training and teaching mainly includes the evaluation of students' physical education academic achievement and the evaluation of physical education curriculum teaching quality. In the comprehensive evaluation of the teaching quality of public physical education in colleges and universities, we should determine the elements of evaluation and the specific evaluation contents scientifically and reasonably. In other words, a physical education teaching quality evaluation system should be determined. e evaluation index is the basic unit of the evaluation system and a relatively fixed parameter.
Based on the evaluation index system and quantification of public sports training quality, this paper designs an evaluation algorithm based on global topology optimization deep learning model to achieve the goal of this paper. Deep learning is a new research direction in the field of machine learning. It is introduced into machine learning to make it closer to the original goal: artificial intelligence. Deep learning is the internal law and representation level of learning sample data. e information obtained in the learning process is very helpful to the interpretation of data such as text, images, and sound. Its ultimate goal is to make the machine have the ability to analyze and learn like human beings and can recognize characters, images, sounds, and other data. Topology optimization is a branch of structure optimization. Structural optimization design was developed in the 1960s. It combines engineering design problems with mathematical optimization methods and produces a method that can solve the optimal engineering problems. According to the different variables of structural topology optimization design, the structural optimization design can be divided into three parts: size optimization,  Journal of Environmental and Public Health shape optimization, and topology optimization [20]. Among the three, topology optimization is the most challenging and practical direction in the current optimization design methods. is structure combines with a deep learning algorithm to form a more advanced training algorithm. erefore, this paper adopts the global topology optimization depth learning model to realize the quality evaluation of public sports training [21]. e basic structure of the global topology optimization deep learning model is shown in Figure 4: e global topology optimization depth learning model is composed of two dense connection layers and four transposed convolution layers. Its output result is the quantitative index data of public sports training quality evaluation obtained above, and the obtained result is the research result [22].
According to the global topology optimization depth learning model set above, determine the state of quantitative indicators for public sports training quality evaluation and effectively evaluate the quality of public sports training [23]. e specific process of public sports training quality evaluation is as follows: Step 1.
rough the global topology optimization, the smoothing factor in the neural network in the deep learning model is the optimization goal, and the optimal model of index evaluation is constructed [24]. Set and initialize the evaluation index data of public sports training quality as follows: e public sports training quality evaluation index is divided into a training set and a test set defined as follows: T test � t m+1 , t m+2 , t m m < n. (11) en, according to the division of the training set, the zero mean standardization method is used to preprocess the public sports training quality evaluation index set, and the following results are obtained [25]: Step 2. Initialize the parameters of the preprocessed training set and randomly generate the initial neurons according to the size [26], location, and population size of neurons in the global topology optimization deep learning model. e neuron set is obtained as follows: Step 3. Calculate the fitness value by integrating constraints and fitness function according to the randomly generated initial neuron set as follows: Step 4. According to the calculated fitness value, search for the optimal fitness value in the global topology optimization deep learning model, that is, y i , the minimum error value erro i between the actual quality value β i and the public sports training quality evaluation index at i time from the prediction starting point, whose expression is as follows: Step 5. Check the conditions for exiting the program to reach the maximum number of iterations or meet the convergence accuracy. If so, output the best result.
Step 6. Update the speed and position of neurons according to the current overall fitness value and individual fitness value.
Step 7. Use the modified linear unit to obtain the construction operation function, that is, where k i+1 describes the evaluation index value obtained when the model network layer, t i describes the weight value obtained when the convolutional layer is used, and v j describes the deep learning network bias parameter, represents the maximum extreme of the running function, and selects the maximum extreme method.
Step 8. Extract the optimal value obtained from the model and use the optimal parameters to establish a global topology optimization depth learning model as follows: where z represents the gradient at time t and φ represents the coefficient of the expected value of g 2 .
Step 9. Enter the training sample data into the global topology optimization deep learning model to train the model. Input the test data into the trained model, output the evaluation quality results in the output layer P, and complete the quality evaluation of public sports training as follows: where U p represents the state of the neural network memory module and h represents the output of the memory module.
In the implementation of public sports training quality evaluation, the zero mean standardization method is used to preprocess the public sports training quality evaluation index set, search for the optimal fitness value in the global topology optimization deep learning model, introduce the global topology optimization deep learning model, input the evaluation index, and output the evaluation quality results.

Experimental Environment and Parameter Setting.
e algorithm design in this experiment is based on the framework of PyTorch 1.2.0, Ubuntu 18.04 system, and two Titan-RTX 16g graphics cards. e network of global topology optimization deep learning model was trained for 50 times of generation selection, and the learning rate of different iterations was changed 0.2 for iteration 1, 0.01 for iteration 60, 0.001 for iteration 80, and 0.0001 for generation 100, and the batch size was set to 1. In the process of public sports training, the samples of full marks of test actions in training data sets are input into the conditional network for training, and various test action template information (feature layer parameters at the last layer of the network) is obtained.
en, this information is used as conditional input, and the score category prediction network is separately trained. e second method is to input all test action samples in the training set into the conditional network for training without screening and then train the score category prediction network separately. In the experiment, students of a physical education major class in a university were selected as the research object. ere were 50 middle school students in the class, and 30 students were randomly selected as the experimental object. Experimental analysis was conducted on the public physical education training programs, teaching contents, and training effects of this major in our school. irty people were divided into two groups: one for the experimental group and the other for the control group. e environment and conditions in the experiment were consistent, and the error value of the evaluation was reduced. e schematic diagram of the training objectives of specific experimental subjects was shown in Figure 5.

Analysis of Experimental
Results. Based on the set experimental environment and relevant parameters, an effective evaluation of the quality of public sports training is carried out. In the experiment, the satisfaction of 30 people in training is investigated to determine whether the selection of experimental indicators determined by this method meets the wishes of the tester. According to the evaluation index of teaching quality of public physical training courses in Table 1, the quality of public physical training is studied.
Based on the contents of Table 1, the statistical results of training quality satisfaction of sample objects are shown in Table 2.
It can be seen from Table 2 that in addition to the objective and realistic conditions, the subjective feelings of the students are also an important basis for evaluation. Students are also the audience, and their satisfaction with public sports training is a direct reflection of the training quality. As can be seen from the data in Table 2, the training facilities have the highest satisfaction level, with the basic satisfaction level above 70%, from which it can be seen that the evaluation indicators selected in this paper have certain feasibility.
In order to further highlight the effectiveness of this method, the experiment compares the accuracy of the sample public sports training quality data evaluation by comparing this method, reference [7] method, and   Journal of Environmental and Public Health reference [8] method many times and measures the accuracy results of the three methods on the sample public sports training quality data evaluation. e results are shown in Figure 6.
By analyzing the experimental results in Figure 6, it can be seen that there are some differences in the accuracy of sample public sports training quality data evaluation by using the methods of this paper, reference [7], and reference [8]. Among them, the accuracy of this method in the evaluation of sample public sports training quality data is higher than the other two methods, and the performance is always relatively stable, higher than 90%, while the evaluation accuracy of the other two methods is lower than that of this method. erefore, it can be seen that the evaluation effect of this method is better.
On the basis of ensuring the accuracy of the above sample public sports training quality data evaluation, the experiment further verified the time cost of this method, reference [7] method, and reference [8] method for the sample public sports training quality data evaluation. e results are shown in Figure 7.
By analyzing the experimental results in Figure 7, it can be seen that there are some differences in the time cost of evaluating the sample public sports training quality data by using the methods of this paper, reference [7], and reference [8]. Among them, from the trend of the curve, the evaluation time of this method is shorter, while the evaluation time of the other two methods is longer. In contrast, the evaluation speed of this method is faster, which verifies the effectiveness of this method.  Evaluation accuracy (%) Figure 6: Accuracy analysis of public sports training quality data evaluation with different methods.

Conclusion
is paper designs a public sports training quality evaluation method based on the global topology optimization depth learning model. Determine the basic principles of public sports training quality evaluation, determine the basic section and basic axis of human training, use the linear camera to determine the human coordinate points of public sports training through the five parameter model, and complete the data extraction of public sports training quality evaluation through three-dimensional matching and coefficient characteristics. On this basis, through the combination of fuzzy comprehensive evaluation method and deep data mining method, the evaluation indexes of public sports training quality are quantified. On this basis, the evaluation matrix is constructed to calculate the weight of the evaluation indexes and determine the importance of the evaluation indexes of public sports training quality. e zero mean standardization method is used to preprocess the public sports training quality evaluation index set, search the optimal fitness value in the global topology optimization deep learning model, introduce the global topology optimization deep learning model, input the evaluation index and output the evaluation quality results, and realize the quality evaluation of public sports training. Although this paper has improved the evaluation accuracy in this study, the evaluation indicators are still incomplete. In the next step, I will continue to explore the evaluation indicators to improve the shortcomings of this method.

Data Availability
e raw data supporting the conclusions of this article will be made available by the author, without undue reservation.

Conflicts of Interest
e author declares that there are no conflicts of interest regarding this work.


Supervisor perspectives in STEM PhD supervision: a social realist analysis of current trends

ABSTRACT The traditional academic pipeline has branched out significantly in recent years. The motivation of students pursuing higher degrees has also changed dramatically in response to wider socio-economic needs. Supervisors of PhD students are consequently faced with the need to adapt supervision practices to equip graduates with a broader suite of workplace competencies. However, PhD supervision is a practice located in the broader setting of a higher education system, conditioned by factors that change over time. In this paper we share the perspectives of a range of STEM supervisors from varying backgrounds on their conceptions of the purpose and goals of the PhD. Archer’s Social Realist theoretical framework was used in a comparative analysis between groups and across themes. It also enabled a novel visualisation in the form of a heat map, illustrating the mechanisms shaping supervision practices, highlighting the strong interplay between student agency and the development of particular dispositions.

Introduction
The higher education landscape has shifted dramatically in the past 20 years. Worldwide, staff and student demographics have changed and the very purpose of higher education is being challenged. The Covid-19 pandemic and rapidly evolving technological landscapes have also accelerated the shift towards an era of supercomplexity (Barnett, 2000), with new skills and competency requirements emerging constantly. There is thus increasing pressure for lecturers to shift postgraduate supervision practices to adequately prepare graduates for the current and future needs of society (Ashwin & Case, 2018). This is particularly true at the doctoral level, where doctoral research and the doctoral qualification is seen as an important means of generating solutions to society's complex problems (CHE -Council on Higher Education Transformation, 2014). Academic posts are also increasingly challenging to secure (Roach & Sauermann, 2017), and many graduates are forced to look outside academia for employment (Walters, Zarifa and Etmanski, 2020).
The branching out of the academic pipeline, with graduates of higher degrees entering workplaces that are vastly different from the academic and research environments for which they have been prepared (Acker & Haque, 2017), indicates the need for doctoral graduates to develop in depth, highly sophisticated understandings of the knowledge of their disciplines and the ability to apply this knowledge to a broad range of contexts and fields of practice. Increasingly, there is also greater emphasis on the 'skills push', where the focus of the PhD is the development of high level skills and competencies (Mowbray & Halse, 2010) as well as critical thinking dispositions (Ennis, 1996;Siegel, 1999) valued by potential employers. At the same time, employment goals may also have implications for the type of identity graduate students develop, based on their intrinsic motivations and their intended career paths. Consequently, doctoral education has needed to adapt in response to the shifting societal pressures and requirements to enable the development of PhD graduates with flexible dispositions and a new type of disciplinary 'gaze' (Bourdieu & Wacquant, 1992) that would allow doctoral graduates to more easily integrate into new contexts and apply their knowledge seamlessly in fields of practice beyond the academe. This shift may, however, present challenges for the development and training of postgraduates especially in Science, Technology, Engineering and Mathematics (STEM) disciplines, particularly for PhD supervisors.
In this paper, we demonstrate how the preparation of PhD graduates, and doctoral supervision practices in particular, has transformed (and may still need to change further) whilst still ensuring that the pursuit of deep disciplinary understanding and knowledge creation that are the hallmarks of the doctoral qualification, is not compromised. Our aim is to share the perspectives gleaned from a range of STEM supervisors from varying backgrounds, ages and experience levels on the changing nature of the PhD and impact of the changes (if any) on their conceptions of the purpose and goals of the PhD and how this might have changed over time. We reveal the factors and mechanisms that influence conceptions of supervision as a professional practice, critically examining the interplay between these factors, highlighting areas of concern and challenges faced by supervisors in their quest to support PhD students in appropriate and responsive ways. The unique contribution of this study is the sharing of insights from supervisors on the current and future objectives of doctoral programs and the use of a heat map to illustrate the mechanisms shaping supervision practices and interaction. The heat map is used to make the salient aspects of the data more accessible, specifically with respect to the resulting intersections of the social realism categories and the interview themes. Understanding the mechanisms shaping supervision practices and interaction, and the interplay between them, could lead to the conceptualization of more effective PhD supervision models, and potentially higher completion rates in the long term.

Motivation for this study
The authors of this paper are all located within STEM fields and are all involved in the supervision of postgraduate students, including PhD students. Although we are in different institutions, it became apparent to us that our experiences of the factors influencing our supervision roles are very similar as a result of being actors within the South African higher education sector. We are all strongly encouraged to take on an ever increasing number of doctoral students, where this comes from government funding models and the stated strategic objectives of related government ministries to increase the number of PhD graduates in the country, the latter being a strategic priority for South Africa (Herman & Sehoole, 2017). We note, however, that while these issues are made overt in the South African higher education sector, these are not unique to our country. Our findings may, therefore, contribute to the broader global higher education community at what appears to be a crucial moment in higher education.
With the above aim in mind, what follows is a brief account of the reported purposes of the PhD, the role of PhD supervisors and currently dominant approaches to postgraduate supervision. An overview of Critical Realism as the overarching theoretical framework, and Social Realism as the analytical framework for this study is then provided, after which we present our approach to the research, our findings and a discussion of the implications thereof for supervision practices and PhD outcomes.

The purpose of PhD studies, PhD supervision roles and models
The PhD, being located at the apex of the higher education qualifications suite, has traditionally been associated with the development and acquisition of particular skills, attributes, competencies and dispositions, drawn largely from the Aristotelian notion of intellectual virtues, including impartiality, courage, open-mindedness, attention, care, honesty, humility, autonomy, perseverance, patience, and industriousness (Mowbray & Halse, 2010). Mowbray and Halse further explain that alongside these intellectual virtues focused on in the PhD is the development of other critical competencies, including the development of in-depth disciplinary knowledge, research skills, critical thinking skills, project management and communication. It is the combination of these intellectual virtues, skills and competencies that makes a PhD candidate attractive for employment in a variety of workplaces.
The role of the PhD supervisor is to attend to the development of disciplinary competence and critical thinking dispositions, acknowledging the many and varied stages of liminality or 'stuckness' that PhD students will experience as they struggle with threshold concepts and troublesome knowledge (Meyer & Land, 2003) that they will encounter in the journey to becoming a PhD graduate (Keefer, 2015;Thomassen, 2009). PhD supervisors are thus also expected to provide an enabling and supportive environment and opportunities to transition students through those spaces of liminality as students develop their disciplinary identities. Linked to this is another fundamental role of the PhD supervisor, viz. enculturation, described by Hockey (1994) as the process of developing disciplinary dispositions and cultural capital (Bourdieu, 1986), and the induction of students into disciplinary communities. This role may be linked to the 'qualities' model of supervision (Wisker, Robinson, Trafford, Lilly, & Warnes, 2003), where the emotional intelligence and flexibility of the supervisor plays a big part in working with students through to a successful completion.
Other styles are that of a mentoring process, described by Pearson and Kayrooz (2004) as a facilitative process requiring support and challenges, with the role of the supervisor as mentor seen as a non-judgmental adviser. Here mentoring builds upon the belief that self-experience and self-discovery are important facets of learning (Morton-Cooper & Palmer, 2000) that lead to emancipatory outcomes (Lee, 2008). Hockey (1994) also describes the functional or project management role played by the supervisor, in agreement with the functional model of supervision which Wisker et al. (2003) define as the supervisor's task as being one of directing and project management. The PhD supervision role thus spans a number of different dimensions (Hockey, 1994), although it can be argued that the traditional supervision roles have been to support the development of intellectual expertise, and enculturation into academic communities.
Although participation in disciplinary-based communities of practice are essential, the PhD journey is, however, largely centered on the interactions and relationship between supervisor and students, underpinned by the individualized traditional apprenticeship approach to supervision inherited from the Oxbridge tradition. As stated by Manathunga (2005), supervision is often seen as a 'private pedagogic space', while Mackinnon (2004) calls it a paternalistic style of supervision which is counterproductive, as it prolongs the dependence of the student on the supervisor. Nevertheless, this approach is still the most favored mode for supervision in South Africa (De Beer & Mason, 2009), where many propose that the untrained supervisor will emulate (or consciously avoid emulating) the qualities that their supervisor demonstrated, as a first instance when supervising.
Whilst supervision is criticized for being seen as an individual space based on individual styles and attitudes without accountability, which may cause problems within the context of increasing numbers, distance and diversity (Wisker, Robinson, & Shacham, 2007), this apprenticeship model is clung to due to the familiarity with most supervisors' experience with this approach, and possibly a lack of knowledge and exposure to other approaches. The isolation characteristic of the apprentice approach (Samara, 2006;Wisker et al., 2007) may, however, be circumvented by using group supervision (Wisker et al., 2007), an approach rapidly gaining traction as the realities of massification emerge in the postgraduate domain.
Group supervision can vary from clustering students of similar levels to those of varying stages, where Wisker et al. (2007) suggest that work-in-progress seminars involving peers encourage weaning of students from supervisors towards creating independence. These group approaches would seem to be translatable to hierarchical knowledge fields such as science and engineering as they enhance the enculturation process. However, this may come at the expense of disciplinary knowledge, as highlighted by Malfroy (2005), where an inexperienced group could be a way of pooling ignorance. Furthermore, Dysthe, Samara, and Westrheim (2006) note that supervision groups in their study operated alongside an individual supervisor process, taking the onus off the individual meeting by saving individual supervision time.
The supervision relationship is thus exceedingly complex and multi-faceted and usually requires inclusion of elements from each model. This is especially true for PhD supervision, where there must be a growth in the relationship from hierarchical to that of equals. Such a shift in relationship is, however, even more complex, influenced partly by the motivations of the students themselves, including the students' end goals which, as already mentioned, are not necessarily to follow in the footsteps of the supervisor and pursue a career as an academic. The process is also influenced by the institutional and national policies as well as a multitude of other factors that intertwine in their influence on supervision practices and student-supervisor relations. The research conducted in this study aimed to unravel these complexities and reveal the dominant factors influencing supervision practices at the present time.

Social realism as a theoretical framework
In light of our focus on supervision practices within the context of the South African and global higher education systems more broadly, we have chosen to underpin this study in the philosophical tradition of Bhaskar's Critical Realism (Archer, Bhaskar, Collier, Lawson, & Norrie, 2013) and to conduct our analyses using the related framework of Social Realism (Archer, 1982(Archer, , 2007Archer & Archer, 1995;Porpora, 2013).
The critical realist strata of the 'real', 'actual' and the 'empirical' have proved useful in attempts by social scientists (and higher education researchers in particular), to unravel and explain changes (or lack thereof) in social systems, including the higher education system. This is because it provides a mechanism through which events and practices may be understood, how it came to be so, and subsequently, how it may be changed. As such, Critical Realism and the related meta-theory of Social Realism by Archer (Archer, 2013;Porpora, 2013), provide useful philosophical and analytical frameworks in this study, for examining supervision interactions and revealing the conditions and factors holding dominant paradigms in place, as well as elements that could be leveraged for change.
Archer (2013) suggests that any change in society or any social grouping (e.g. higher education) may be due to a variety of factors arising in the realm of the 'real' that result in the emergence of new phenomena and events in the realms of the 'actual' and the 'empirical'. In attempting to understand the nature of postgraduate supervision and possibilities for change, researchers may make assumptions about the structure of the higher education system within which supervision occurs and how these condition supervision practices. Practices are however also influenced by individual perceptions and beliefs. Explanations of change based on consideration of structures alone may, therefore, not be a true reflection (or possibly an underestimation or overestimation) of the 'real' causes or conditions. Social Realism addresses the potential challenge of underor over-estimation (termed 'epistemic fallacies' (Archer, 2000)) by providing a framework that allows the higher education researcher to analyze how complex effects or social events arise within the higher education system, by independently examining the existence and interplay between people (agents) and the 'parts' (structure and cultural) in any social system/society (Archer, 2013). Archer posits that to understand each component in-depth and to sequentially analyze their influences on each other and on the system as a whole, structure and culture must be separated from agency. This separation for the sake of analysis is referred to as 'analytical dualism' (Archer, 2013;Porpora, 2013).
Applying social realism to the practice of postgraduate supervision, the structures influencing supervision may include institutions such as universities and government, as well as national and institutional policies, as indicated in a recent publication by Motshoane and McKenna (2021). Social structures may also include position and status or social class (of supervisors and students), gender and race, as well as material resources such as funding. Culture, on the other hand, refers to the ideas, beliefs, values and ideologies held by individuals that have been learned (through social interactions or through reading and contextualizing information), and which may be shared with others (Archer, 2013;Case, 2013). It may also include the sets of ideas on what constitutes legitimate knowledge in a given context (a PhD in Digital Arts in the Humanities, for example, would be underpinned by very different knowledge typologies compared with a PhD in Theoretical Physics within the Sciences (Archer, 2013;Case, 2013)). Human agency, in comparison, refers to the personal and psychological makeup of individuals, their social roles and their capacity to act in different ways to defend their interests and realize their goals, driven by personal motivations and values (i.e. culture) (Case, 2013;Porpora, 2013). PhD students and their approaches to the qualification may thus be conditioned not only by disciplinary knowledge, norms and practices, but also by students' own intrinsic motivations; while the approaches and actions of PhD supervisors too, will also be governed by their own intrinsic values and beliefs about the purpose and nature of the PhD.
Given the complexities of PhD supervision, Social Realism, thus, proved to be an ideal analytical framework for examining, identifying and categorizing the factors and mechanisms influencing supervision practices amongst participants, and the resultant effects on the student-supervisor relationship and outcomes of the PhD.

Methodology
The study followed a qualitative research approach, beginning with a narrative inquiry on supervisory experiences and practices between the three authors of the paper. This initial enquiry led to the identification of the overarching research question and thematic focus areas for more in-depth enquiry and analysis. We then obtained ethics clearance to invite participants from STEM disciplines in two different institutions (2020-09-04/Cornell), thus creating a broad pool of supervisors to interview. Ethical consent for the study was granted by the institutional ethics committees of both institutions. Based on the responses to the invitation to participate, three separate semi-structured group interviews were conducted online with consenting participants from both universities. All participating supervisors provided informed consent.
Each interview group consisted of 4 participants with different levels of experience and from different disciplinary backgrounds. For example, group one included a participant from biology, a participant from mathematics, and 2 participants from engineering. In total, there was representation from physics, mathematics, engineering, biology, and computer science fields, allowing for further refinement of the STEM field's perspectives on supervisory practices (specifically from the supervisor's perspective). The group interviews were facilitated by the third author who has experience in group interview facilitation, guided by the themed questions contained in the interview schedule approved by the ethics committee, with co-facilitation from the other two authors.
During the group interviews, participants were asked about their experiences and perspectives with respect to the four thematic focus areas. These focus areas included their motivations for pursuing a PhD compared with their perceptions of current students' motivations; perceived constraints; time to completion, and their view on liminality and the development of PhD dispositions. The questions and discussions were thus intended to not only get the participants' perspectives as supervisors, but also their experiences -with respect to the focus areas -as PhD students themselves. In this way, we aimed to elicit responses whereby participants would compare and contrast their own PhD experiences with their perspectives on their students' PhD experiences.
The interviews were recorded with permission from all participants, and all participants were anonymised by replacing their names with pseudonyms -e.g. Supervisor 1.
These were then transcribed and systematically coded relative to the four focus areas underpinning the study, viz., Motivations for pursuing the PhD, Time to Completion, Liminality and Dispositions and Constraints. The responses in these themes were then further analyzed and coded against the social realist categories of Structure, Culture and Agency. The coding allows for collation and analysis of the supervisors' experiences with respect to the intersections of study themes and categories, and protects the anonymity of the individual supervisors. The study also presents specific responses from participants to further explain or exemplify significant patterns found in intersecting themes and categories. This approach not only enabled a route for conducting a comparative analysis between groups, across themes and categories, but also allowed for a visual representation in the form of a heat map (shown in Figure 1), thus making the salient aspects of the data more accessible. Figure 1, presented in the Findings and Discussion section, is a heat map that gives a visual representation of the frequencies with which responses from interviewed supervisors appeared within the intersections of categories and themes, as further discussed in the next section. To obtain this visualization of interplay between the categories and themes, individual responses of participants from the interviews were firstly coded. The responses to questions under each theme (on the vertical axis of the heat map) were recorded, analyzed and categorized in terms of their relevance to one or more of the social realist categories (on the horizontal axis of the heat map). For example, if a participant responded to a question of reasons for pursuing a PhD (under the motivations theme) as being related to cultural motivations, then this was counted as an intersection between the motivation theme and culture category. The total number of responses within each of these intersections were then recorded and normalized against the number of questions in each theme (as the total number of questions under each theme varied) to obtain the overall frequency with which each of the social realist categories intersected each of the study themes. These frequencies are visually represented as a color gradient on the heat map, illustrating the spectrum of different frequencies of participants' responses that amounted to an interplay between themes and categories. A darker shaded block represents more responses in the specific socialist realism category within a particular study theme, whereas a lighter shaded block represents relatively lower frequencies of intersecting themes and categories. It should be noted that the substance of individual responses falling outside the specified themes and categories used in the process of generating the heat map were not disregarded in the study because of lower frequencies. Such points were still deemed important and were considered in the more general findings and interpretations. We also highlight that the decision to use the heat map was based on it serving as a mechanism to prioritize the factors influencing conceptions of PhD supervision as a professional practice, and the interplay between these.

Findings and discussion
The analytical approach adopted in this study enabled the collation of responses and a nuanced categorization of these into each theme of questions, relative to the categories of structure, culture and agency. This categorization revealed the highest number of responses to be those within the intersection of the theme of Liminality and Dispositions and the social realist category of Agency (Figure 1). In this respect, liminality was highlighted by participants' accounts of moments of cognitive disruption and dissonance experienced by doctoral students as they engaged with threshold concepts, dispositions being those critical thinking skills and attributes that students develop in this cognitively challenging process, and agency signified by statements related to students' personal goals, motivations and values for the PhD qualification. Figure 1 also highlights the intersection of the category of structure and the theme of constraints as the second highest area of significance for supervisors, and the areas of intersection between structure and time to completion, as well as agency and motivation as two other note-worthy areas for supervisors. These intersections are discussed next, where we examine factors and mechanisms operating at these intersections that influence supervision practices, as extracted from responses from the group interviews.

Motivation and agency
During the group interviews, the discussions were initiated with questions directed towards an exploration of the supervisor's own motivations for pursuing a PhD, followed by their views on their students' motivations for registering for a PhD and a reflection on how these motivations might (or might not) be different from their own. The responses from supervisors suggest a shift over time in terms of what factors motivate pursuing a PhD. Most supervisors, particularly more experienced supervisors, expressed their motivations as being intellectual curiosity and/or personal background, i.e. coming from a scholarly family, primarily suggesting factors in the agency and cultural categories as main motivators. These motivating factors contrasted sharply with the majority of the supervisors' experiences of what motivated their students to pursue a PhD. Responses to the latter suggested that obtaining transferable skills, career advancement, competitive edge in industry settings, through the qualification, were some of the key motivations for students pursuing a PhD. This links directly with our aim of how motivations have changed over time, highlighting the greater frequency of responses in Figure 1. Supervisor 10, for example, expressed their own experience, highlighting a key motivation for their students pursuing a PhD: 'They want the end result, they don't necessarily want to be scientists. They want the qualification that will give them a good job. ' This sentiment was shared by Supervisor 3, commenting that, 'They want to be competitive when they go to industry', when referring to their students' motivations for registering for a PhD.
The strong link between motivation and individual agency was not unexpected. Higher degrees are seen as leverage for better employment options and higher levels of remuneration. However, it was not the sole reason cited for students registering for PhDs. Four of the interviewed supervisors expressed that there are some students, albeit fewer than in the past, who shared their own motivations for pursuing a PhD (such as interest and curiosity), as seen in the quote below from Supervisor 7: 'In my opinion, it's a mix of those with genuine curiosity and those that want to achieve a higher qualification.' This quote demonstrates the value placed on intellectual curiosity and thinking dispositions described by Mowbray and Halse (2010) that PhD graduates are believed to possess, and which relate closely with the theme of liminality and dispositions.

Agency in relation to Liminality and dispositions
This theme was explored during interviews to ascertain supervisors' perspectives on the conceptual and cognitive 'boundaries' that PhD students usually transition across during their candidature, which are, in turn, linked to the sought-after graduate attributes and dispositions valued by employers. As mentioned, the overwhelming majority of interviewees pointed out factors in the agency category. Figure 1 illustrates this point, showing the relatively high frequency with which participants' responses eluded to agency within the liminality and dispositions theme. All supervisors notably referenced elements of students' attitudes as being the main factor influencing their ability to transcend moments of conceptual struggle and liminality (Keefer, 2015), citing independence, initiative, commitment and resilience as key elements required in this process. Interestingly, independence was also regarded as an important disposition and outcome of the PhD process, with most participants recognizing independent thought and action as a clear indication of PhD graduateness. This consensus amongst interviewed supervisors around independence as being an important disposition is typified by the following statement from Supervisor 4: ' . . . when we look for the key dispositions, I would say its independence, . . . and this is when I have the "aha moment" that the student is now thinking like a PhD'.
The intersection between liminality and the development of dispositions such as independence and industriousness was, however, not surprising, given that these attributes are part of the suite of intellectual virtues associated with the PhD (Mowbray & Halse, 2010). It would seem, therefore, that the transition to being a graduate-worthy candidate is largely dependent on student agency, with the role of the supervisor being to support and challenge the student in the process. We note however that some participants, particularly the more experienced supervisors, agreed that there has been a notable change in supervision practice as a result of shifts in student expectations of supervision over time, and that students seem to require more supervisory input now than in the past. The findings also link to our stated aim of analyzing the changing nature of the PhD as it revealed that although student agency tended to be the most prominent underlying factor for motivation and successful completion of a PhD, there was also a broader influence of other social realist categories viz., structure and culture, that influenced PhD outcomes and supervision practices, as discussed next.

Structure and time-to-completion
When participants were asked about their perspectives and experiences with regard to constraints, the most commonly reported issues related to time-to-completion. Figure 1 illustrates the relatively high frequency with which participants' responses eluded to structural factors within the time-to-completion theme. Participants reported significant and increasing pressures on supervisors to minimize the time to completion. These were reported to be institutional pressure which, in turn, was directly related to funding and government subsidies.
An example of institutional pressure highlighted by interviewed supervisors, the inclusion of PhD supervision as a key performance indicator in the evaluation of the academic, whereby time-to-completion was noted to have become a salient factor in managing performance. Supervisor 1 expressed this as a mechanism that 'changes the behavior and practices of supervisors',: ' . . . this changes the student-supervisor relationship. It means I have to measure beforehand if a student is likely to complete on time . . . ' Supervisor 4 expressed that the importance of time-to-completion depends on whom you ask, but shared the overall sentiment across the groups with respect to institutional pressures: ' . . . certainly our administration is on our case to reduce the time-to-completion . . . ' The emergence of time to completion as a significant challenge is supported by the findings of Carter, Kensington-Miller, and Courtney (2017), in their study of the problems of doctoral supervision practices. Those authors reported on the impact of time constraints imposed by regulatory bodies on the level of engagement, the quality of the research and the overall enjoyment of the doctoral process. Similarly, Bastalich (2017Bastalich ( , p. 1145) highlighted a 'core premise within much policy and management discourse around doctoral education in the non-US context is that supervision is the key to both quality and efficiency in higher degree research'. With the massification of higher degrees, this premise has manifested in intense scrutiny of doctoral supervision within cost efficiency drives in which supervisors are often blamed for unsatisfactory completion times and high withdrawal rates. Participants in the present study also highlighted that the pressure to get students to complete in minimal time strongly constrains their supervision practices. This pressure has been increasing over time, impacting the type of projects that can be pursued and therefore the in-depth disciplinary nature of the PhD going forward. Interestingly, a supervisor in the present study also referred to the knockon effect of the emphasis on reduced time to completion on supervisor reputation, stating that the negative perceptions associated with having graduates with longer times to completion tended to be viewed as poor supervision ability. The impact of steadily decreasing time to completion imposed by higher education regulators thus presents a significant challenge for both students and staff.
Another critical issue that emerged as a result of pressure to ensure that students complete in minimal time (which has been significantly reduced over time) is a concerning element of gatekeeping at the point of accepting the student into the qualification. Some participants pointed out that supervisors increasingly have to base the decisions of whether to accept a PhD applicant on a tacit evaluation of whether the applicant has the dispositions described earlier -dispositions that could (and should), with sufficient time and appropriate supervision, develop during the PhD. Participants in the focus group in which this point emerged strongly acknowledged this evaluation of potential students as problematic, with possible unintended negative consequences for access and broader transformation goals, particularly in contexts such as South Africa, with legacies of educational inequality.

Structural factors and supervision constraints
Supervisors were asked to describe some of the foremost constraints they experienced as PhD supervisors. Supervisors' responses under this theme were most frequently related to factors in the structural and cultural categories. This is illustrated in Figure 1, where the darker shading of the intersecting block demonstrates the relatively high frequency with which participants' responses eluded to structural factors within the constraints theme. In addition to pressure related to time-to-completion, another prominent structural factor expressed as a constraint by supervisors was the lack of funding. This finding is again in line with Carter et al. (2017), who also reported the negative effects of financial constraints. In the present study, supervisors who were interviewed lamented the fact that finances often present serious challenges both to students as well as their own capacity in terms of providing research capabilities, i.e. research laboratories and spaces, and equipment. In the current economic climate, national and private funding mechanisms for research are extremely limited and likely to dwindle even further in the wake of the Covid 19 pandemic, with serious consequences for PhD student and project support.
Another significant structural challenge was the issue of massification and the pressure to grow postgraduate numbers. Supervisor 1 analogized this to a 'sausage factory approach', a view that was shared by most participants interviewed. This suggests a perception of increasing constraints on research creativity, a notion also highlighted by Hockey (1994). The drive to supervise more students coupled with dwindling resources and the push for students to complete in minimal time has reportedly placed serious constraints on the ability of supervisors to engage with PhD students in the ways that they would prefer, and may impact negatively on the supervision relationship, with participants reporting that they are increasingly unable to give students the attention they need. There also seems to be a shift in supervision models as a result, with less time for direct supervisor-student interaction characteristic of the apprenticeship and mentorship models that have traditionally dominated STEM fields (De Beer & Mason, 2009), towards greater use of group and team supervision. As pointed out by Malfroy (2005), this shift to group and team supervision may potentially result in loss of quality of outcomes if supervisors, as the disciplinary experts, are significantly less involved.
The structural influences on PhD supervision have also impacted institutional culture, with greater competitiveness and increasing lack of collegiality negatively emerging as a consequence in some instances. Seen through the lens of social realism, we argue that this observation may be seen as evidence of structural mechanisms leading to structural elaboration in the first instance, and a second phase of cultural conditioning and cultural elaboration. This is akin to Rönnerman and Kemmis (2016) demonstration of institutional practice architecture shaping, limiting and holding supervision practices. The present study provides further evidence of the impact of these institutional structural and cultural mechanisms on supervisors and doctoral students who find their agency constrained as a result.

Conclusion
Our study allowed us to demonstrate the dominant factors currently shaping postgraduate supervision, which has already undergone significant shifts in the last 20 to 30 years, alongside the emergence of the neoliberal university. As we had stated earlier, our aim was to share perspectives gleaned from a range of STEM supervisors from varying backgrounds on the changing nature of the PhD, and the potential impacts of these changes on practice. Our results show a distinct shift in the degree of self motivation that supervisors perceived of themselves, compared with their current students, and a related perception of current PhD students expecting more supervisory input. However, while supervisors acknowledge the need for more support and different types of supervisory input, structural constraints such time limitations and ever increasing student numbers may severely constrain the amount of additional input that can be provided. In addition, institutional culture and increasing competition may further constrain supervision practices. We thus demonstrate from this exercise that we appear to have reached a tipping point where the existing constraints are beginning to impact quality in a potentially very negative way, curbing agency, particularly of the supervisors, to adapt practices and better prepare graduates. Our findings, although limited in scale, are in line with the broader body of literature on supervision, which is increasingly critical of the ways in which the personal agency of supervisors and students is increasingly being constrained by bureaucracy. What we have to determine next, is the potential pivot points that can be leveraged for positive change, while being fully cognizant of existing constraints and the potential consequences of changing the status quo.