Precarious Academics: Information Practices and Challenges
Bibliographic record
Abstract
This paper reports the results of a small-scale study of the information practices of contract academic staff in the United Kingdom, which is being used as the basis for a broader study in the Canadian context. Neoliberal approaches to the management of higher education across the globe, including Canada, are contributing to a highly challenging environment for contract academic staff, who face marginalization, insecurity, and significant stress. The study seeks togive voice to this growing complement of contract academic staff, to identify practical responses to these challenges. Cet article présente les résultats d'une étude à petite échelle des pratiques d'information du personnel universitaire contractuel au Royaume-Uni, qui sert de base à une étude plus large dans le contexte canadien. Les approches néolibérales de la gestion de l'enseignement supérieur à travers le monde, y compris au Canada, contribuent à créer un environnement très difficile pour le personnel académique contractuel, confronté à la marginalisation, à l'insécurité et à un stress important. L'étude cherche à donner la parole à ce complément croissant de personnel académique contractuel, afin d'identifier des réponses pratiques à ces défis.
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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 | 0.032 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.020 | 0.014 |
| Scholarly communication | 0.025 | 0.008 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".