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Record W4408938057 · doi:10.1111/irj.12466

Academic Capitalism and Precarity in the Neoliberal University: Job Insecurity and Stress in Two Liberal Market Economies

2025· article· en· W4408938057 on OpenAlexafffundabout
Sean O’Brady, Greg J. Bamber, Brian Cooper

Bibliographic record

VenueIndustrial Relations Journal · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsMcMaster University
FundersMcMaster University
KeywordsPrecarityCapitalismJob insecurityJob marketPrecarious workNeoliberalism (international relations)EconomicsLabour economicsMarket economyPolitical economyPolitical scienceWork (physics)

Abstract

fetched live from OpenAlex

ABSTRACT This study analyses the relationship between academic capitalism and employment precarity. Drawing on two cross‐national sources of survey data, we compare academics' experiences with job insecurity and related stress in Australian and Canadian universities during the COVID‐19 pandemic. Although these two countries are similar liberal market economies, Australian higher education has embraced academic capitalism to a greater extent than in Canada. Against this backdrop, our study focuses on the moderating role of ‘soft income’ from international tuition fees and the use of contingent labour in explaining cross‐country differences. We find that international tuition played a role in heightening job insecurity and associated stress, particularly in the Australian universities due to their greater reliance on this source of income. However, these outcomes converged for ‘permanent’ and ‘casual’ academics in Australia but diverged in Canada. We contend that the cross‐national differentiation is likely due to the weaker job protections afforded to permanent academics in Australia.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.997
Threshold uncertainty score0.487

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.003
Scholarly communication0.0030.001
Open science0.0000.004
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.028
GPT teacher head0.316
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2025
Admission routes3
Has abstractyes

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