Reimagining Writing Centres Practices: A South African Perspective
Bibliographic record
Abstract
In light of the changing face and internationalisation of our student body and their concomitant needs, this book attempts to foreground both the strides made in the field, as well as the important questions and debates confronting writing centre practitioners in the South African higher education arena. The latter demands that we review and reimagine the support we currently provide. Reimaging, however, forces us to wrestle with the challenges that are inherent in work of this nature and to be vocal about the difficult questions that must be asked and answered if we want to provide socially just solutions to our students’ writing challenges. The onset of COVID-19 also imposed on our daily practices and required a hasty re-evaluation of our service provision. The aim of this volume is to further conversations and research on the notion of the internationalisation of writing centres and the necessity to focus on the key issues of multilingualism, discipline-based writing, social justice, the impact of the COVID-19 pandemic, as well as specialised consultant/tutor training. Writing centres at South African universities have established themselves as fundamental to the support and development of our students. Thus, the time is ripe for us as writing centre practitioners in the South African context to continue writing our own writing centre narrative, grapple with context-specific issues and questions, and provide context-specific answers and solutions that speak to the lived realities of our students. We hope to achieve this through this book.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.028 | 0.039 |
| Scholarly communication | 0.020 | 0.016 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".