Examining the impacts of a labour disruption on first-year students: Workload, interest, and understanding of course content
Bibliographic record
Abstract
York University experienced a labour disruption in winter 2024. Most courses’ modes of delivery were modified. All courses were impacted due to contract faculty on strike. This affected students’ workload, interest in their courses, and understanding of course materials. While work on these variables exists, little research exists on how they change during a labour disruption. We aim to understand how workload, interest levels, and understanding of course content changed week-by-week and semester-wide. Through workload calculators and student-response surveys, we quantified the variables, both before and during the strike. The resulting week-by-week graphs showed that most courses decreased in workload and engagement during the strike, likely due to reduced accountability for submitting deliverables on time. Suspended courses and lab/tutorial-heavy courses were heavily impacted, with courses that included tests/assignments/projects being less impacted. Therefore, we propose that strike-impacted courses should maintain accountability for deliverables, and the deliverables themselves should not solely be labs and tutorials.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".