‘There’s no onboarding, no orientation:’ the role of neoliberal university structures in the lives of precarious academics
Bibliographic record
Abstract
Introduction. Increasingly, universities rely on casualised labour for teaching and research activities. Previous research demonstrates that the information practices of academics working in these conditions are significantly compromised, making their information work more difficult. This paper further explores the context in which contract academic staff work and provides a more holistic picture of their information environment. Method. To gain a better understanding of the academic context, semi-structured interviews were conducted with twelve heads of academic units working in various disciplines and across Canadian universities. Analysis. Interview data were analysed using reflexive thematic analysis, informed by the theoretical frameworks of neoliberalism and information marginalisation. Results. Two themes were generated from the analysis. 1. Casualisation destabilises and creates divisions. Relying on casualised labour means more and shifting work for permanent staff and the creation of unequal power relationships based between permanent and casualised academics. 2. Contractual status influences access to information and departmental engagement. Based on their contract status, casualised academics are often excluded from the places where workplace information, limiting their access to information. Conclusion. Neoliberalism and information marginalisation provide frameworks to better understand complex information environments and the systemic issues that influence information practices.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.075 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".