“Dedicated Drop-ins” as a Way of Addressing Some Writing Centre Challenges
Bibliographic record
Abstract
Writing centres need to be integrated into the writing community of their host institutions, but this can be difficult: often students view them as peripheral (Bowles 2019), see them as “fix-it” shops and/or see them as places where one simply “learns to write” (Cheatle & Bullerjahn, 2015; Simpson 2010), or do not perceive a connection between their services and students’ actual, current course work (Missakian, Olson, Black & Matuchniak, 2016). In this article I discuss the practice of offering and running “dedicated drop-ins,” course- and assignment-specific drop-in sessions for writing support, as one means of addressing several of the challenges that writing centres face in terms of making themselves visible and visibly useful members of their institutional community. Our experience shows that while these “dedicated drop-ins” are not in themselves a perfect solution, they can be a useful addition to writing centres’ toolkits.
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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 | 0.021 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.042 | 0.011 |
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".