Precarious Professionals: Non-Tenure-Track Faculty in Southern Ontario Universities
Bibliographic record
Abstract
While precarious work is a phenomenon often associated with non-professional workers, the emerging case of non-tenure-track faculty (NTTF) calls for a new framework building on scholarship on both precarious work and the professions. An in-depth case study of NTTF in southern Ontario shows how a new phenomenon of ‘precarious professionals’ is emerging. Drawing on sixty semi-structured interviews with faculty members, university administrators and union representatives across southern Ontario, I analyze workers’ experiences in temporary contract work in the academic profession, and their views on the way certain types of professional work are valued. Building off previous literature on precarious work, gender and work, and professional work, this thesis defines precarious professionals as highly skilled workers who do professional work that is valued and devalued along lines of gender. Their experiences in temporary contract work marginalize them economically and professionally in complex and compounding ways that trap them between identifying as precarious workers and as professionals. Union organizers and activists draw on a two-pronged approach that addresses both dimensions of precarious worker and professional identities. This thesis shows variation in workers’ experiences, suggesting that not all temporary contract workers become precarious professionals, and shows how that variation can be explained.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.020 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".