Bibliographic record
Abstract
In this article, we discuss a writing support strategy called Writing Office Hours (WOHs), which has been one of the offerings provided by the Robert Gillespie Academic Skills Centre (RGASC) at the University of Toronto Mississauga since the beginning of the COVID 19 pandemic in March 2020. WOHs evolved out of an earlier approach to course support known as Dedicated Drop-ins (DDIs), which were were made impossible due to the pandemic. In this article, we argue that these WOHs have had enthusiastic uptake by students (at least in part) because they help to break down some of the barriers—physical, logistical, psychological, and/or cultural—that can dissuade students from seeking out writing support. It is widely recognized that students do not always see writing centres as safe and welcoming spaces (e.g., Bond, 2019; Denny, 2010; Grutsch McKinney, 2013; Pregent, Williams, Marcyk, & Haywood, 2021). As we discuss below, because WOHs take writing support out of the centre and into students’ course shells, through its learning management system (LMS) (e.g., Brightspace), they help us reach students who might not (yet) see the writing centre as the “cozy” (Grutsch McKinney, 2013) place that we as writing centre faculty would like it to be.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.244 | 0.131 |
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".