The Fall Pause: An Overview of the Current Canadian University Landscape and a Review of the Related Emerging Literature
Bibliographic record
Abstract
In recent years, many Canadian universities have added a Fall Pause to their academic calendars. However, those who have been making these decisions have been doing so without having access to any resources that provide an overview of existing Fall Pause practices or models across the nation. Additionally, there exists a paucity of literature that provides a sound rationale for the introduction of a Fall Pause. This paucity of literature makes research-informed decision making about the introduction of a Fall Pause an especially difficult task. Given these observations, we have undertaken the task of writing this article with two goals in mind: (a) to provide an overview of the current Canadian university landscape with respect to the Fall Pause; and (b) to provide a scoping review of the related emerging literature related to the Fall Pause. With this information made available, it is our hope that university faculty and administrators will be better positioned to make informed decisions about the possible introduction or continued inclusion of a Fall Pause in their own universities’ schedules. They, and we, might also then be able to have a better (informed) sense of the research-based outcomes for those who have already introduced and/or experienced a Fall Pause.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.019 | 0.035 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".