Bibliographic record
Abstract
This teaching report describes a workshop delivered at the University of Toronto Mississauga as a part of the Robert Gillespie Academic Skills Centre’s (RGASC) Head Start program. The workshop was premised on two guiding ideas: (1) since the University of Toronto maintains flexible guidelines regarding generative AI (hereafter genAI) policies across courses, undergraduate students benefit from participation in candid discussions of the contextual nature of shifting technological values and (2) first-year university students are in the unique position of also needing to contextualize the shift from high school to university learning contexts, so they are in particular need of opportunities to discuss the diversity of perspectives surrounding the permissibility of genAI use in higher education. The workshop led students through noticing the differences between high school and university learning expectations; applying socially oriented theories of communication; contextualizing “local” genAI syllabus policies; and crafting a personal theory of acceptable genAI use. This report is a collaboration between an undergraduate student (Author 1) and a writing professor (Author 2). To support educators in replicating all or part of this exercise within their own local contexts, workshop materials are appended.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.496 | 0.340 |
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".