Supporting Academic Integrity in the Writing Centre: Perspectives of Student Consultants
Bibliographic record
Abstract
Abstract Writing centres are often described as safe spaces where students can explore their ideas and concerns, including questions about how to use and cite sources without plagiarizing. In many Canadian writing centres, these issues are addressed by student consultants who provide effective and influential peer-to-peer support in individual consultations. Little research, however, has directly examined the perspectives of student consultants in providing academic integrity support. This chapter provides a synthesis of what literature currently exists on the role of student consultants in supporting academic integrity before describing a case study with student writing consultants at the University of Guelph. Using data gathered through a survey, this chapter examines the experience and perceptions of student consultants in providing academic integrity support. The findings suggest that academic integrity conversations often arise indirectly, through conversations about referencing or paraphrasing. Student writing consultants consistently position themselves as intermediaries, helping protect students from academic misconduct by using a range of directive and non-directive strategies.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.013 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| 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".