Tenurable Teaching-Focused Faculty Streams and Ranks in Canadian Universities: A Review of Collective Agreements
Bibliographic record
Abstract
As in several other countries, Canada’s research-focused universities have seen an expansion in recent decades of tenurable or tenure-like teaching-focused faculty (TTFF) roles. TTFF streams are not new in Canada, but they have experienced growth in number and re-definition in scope at many universities. Despite its definitive impact in university/faculty relations, the language of negotiated collective agreements (contracts) has not been considered on a national scale. This study examined collective agreements in place on January 1, 2023, at 15 medical/doctoral and 15 comprehensive universities in Canada to determine whether they had TTFF positions. We investigated how these positions compared across institution types based on key features including definitions, duties, and workloads, promotion and tenure criteria, professional mobility and complement limits, sabbatical leave, and salary provisions. Findings indicate that TTFF streams were present in eight of the medical/doctoral universities and seven of the comprehensive universities. We conclude that tenurable or tenure-like teaching streams are no longer anomalous in Canada, and that while many universities construct these positions as teaching-only, TTFF contract language increasingly encompasses scholarly and curricular work outside the classroom with an identifiable impact on teaching and learning beyond one’s own students.
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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 | 0.022 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.015 | 0.030 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| 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".