Adjuncting for Life: The Gendered Experience of Adjunct Instructors in Ontario
Bibliographic record
Abstract
The market-based imperatives driving economic growth in Western societies have, in ways, both been acknowledged and implicitly used to reorient public institutions - academia dramatically so. This article deals with upending of post-secondary academic hiring priorities, and the impact on the adjunct or sessional lecturers implicated in the change. Over half of the courses offered by academic departments and programs in Ontario, Canada, are now taught by part-time faculty members (Pasma & Shakes, 2018). Their use in post-secondary education is underpinned by a notion of just-in-time course delivery in a free market of untenured PhD holders. This study assessed 26 adjuncts in Ontario, Canada, equally divided between male and female. It found working conditions, development of research dossiers, and health and work-life balance to be characterized by gendered differences and hardships. Although the hardships of post-PhD adjunct work have been abundantly documented, this work brings to light components of the experience that have not been previously studied, most significantly, its health effects and gender nature. It concludes with policy recommendations to support adjuncts in Ontario and beyond, including mentorship, longer term contracts, institutional research funding, extended health benefits, and affordable childcare.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".