Swerve & Shift: The lived experience of Canadian faculty teaching through a pandemic
Bibliographic record
Abstract
During the COVID-19 pandemic, which began to affect Canadian universities and colleges in March 2020, educators were asked to make a sudden transition, or swerve, to teaching remotely. Subsequently, these same teachers made the shift to teaching remotely longer term, through the fall and winter of 2020-21; the pandemic continued to affect higher education into 2022. Through interactive interviews, the authors have woven together the individual experiences of teachers in higher education across Canada, and found that these teachers faced unprecedented challenges, as well as unimagined silver linings. Based on Porges’ (1995) model of polyvagal theory, the authors suggest that digital communities of practice (Donaldson 2020) provided a muchneeded site of social engagement to mitigate trauma in these unprecedented times.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.076 | 0.043 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".