Academic casualisation and precarity: a scoping review
Bibliographic record
Abstract
Worldwide there are significant and growing concerns about the increasing number of academics working on short-term contracts (referred to as adjunct faculty, contingent faculty, casual academics, sessionals, etc.). These concerns include working conditions and the consequences of casualised labour practices on higher education. However, the number of empirical studies that speak directly to these issues is relatively small. This scoping review, which is the first review of the experiences of contract academic staff, used Arksey and O’Malley’s methodology for a scoping review and used PRISMA guidelines. Twelve databases were searched, and 2507 records screened, leading to 71 empirical articles focusing on the experiences and perceptions of contract academics, including their ways of working, communications and interactions with universities, and the influence of precarity and marginalisation within higher education, generally. The findings show working on short-term contracts not only disrupts how academics conduct their day-to-day work, but also influences their expectations about academic work. Further, the findings indicate that precarity is differentially experienced, leading to greater inequality for some. Lastly, the findings point to disrupted workplace communications. The review highlights both practical issues for contract academic staff and broader concerns for the field of higher education.
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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 | 0.019 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.028 | 0.028 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".