Academic Literacies in a South African Writing Centre: Student Perspectives on Established Practices
Bibliographic record
Abstract
Through a case study conducted in 2014 and 2015 at the University of X in South Africa, the researchers collected focus group and survey data to develop a better understanding of the kinds of students who use the university’s Writing Centre and their perceptions of the support they receive. The research question at the core of their study asks whether a South African writing centre’s academic literacies practices and philosophy should be adapted or changed to better serve today’s students. The results of the study demonstrate that the vast majority of students who visit the writing centre speak English as an additional language and believe they need more writing support with a focus on lower order concerns than that currently offered through the academic literacies approach at the university. The researchers concluded that the South African undergraduate students at the University of X need differentiated forms of writing support that go beyond the orthodoxies of the current academic literacies approach embraced by the University’s writing centres. The researchers urge writing centres to acknowledge the need to develop interventions and models of support that target English as an Additional Language (EAL) students without adopting a deficit-perspective and without abandoning the long-term project of challenging the privileged status of the English language within the institution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.026 | 0.018 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".