Extent and Consequences of Faculty Members’ Workload Creep in Three Canadian Faculties of Education
Bibliographic record
Abstract
The literature suggests that the emergence of market metrics in the administration of major research universities has led to an increase in workload, here called workload creep, among faculty members in academia. Addressing the research question “What is the evidence and impact of workload creep on faculty members in faculties of education in Canada?” this article begins to address the lack of empirical evidence addressing the scope and consequences of Canadian faculty members’ workload. To date, most research on the workload of Canadian higher education faculty is conceptual in nature, limited methodologically, or conflates data from multiple disciplinary areas. This research is different, focusing on faculties of education in three demographically similar U15 Group of Canadian Research Universities. Through analysis of qualitative in-depth interviews and comparison with research in different contexts, this article reports on the perceived personal and professional consequences of workload creep in terms of faculty members’ mental health, physical health, and productivity. Workload creep undermines traditional notions of valued academic identities. Keywords: workload, higher education, faculty, academic identity
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.021 | 0.008 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".