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Record W3149270104 · doi:10.1177/10353046211003635

Work reorganization in the neoliberal university: A labour process perspective

2021· article· en· W3149270104 on OpenAlexafffund
Stephanie Ross, Larry Savage

Bibliographic record

VenueThe Economic and Labour Relations Review · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical and Economic history of UK and US
Canadian institutionsBrock UniversityMcMaster University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPrecarityNeoliberalism (international relations)Context (archaeology)PaceHierarchySubject (documents)Political sciencePoliticsSociologyWork (physics)Perspective (graphical)Process (computing)Political economyEconomic systemEconomicsGender studiesEngineering

Abstract

fetched live from OpenAlex

This article maps the major changes taking place in academic work within the broader context of the neoliberalisation of universities. Recognising the great variability in the form and pace of neoliberalisation across institutions and national contexts, the article identifies a set of features and indicators to aid in the comparative assessment of the extent and effect of neoliberal processes at different institutions. The authors use conceptual tools from labour process theory to highlight the ways that neoliberalisation has resulted in academic work that is fragmented, deskilled, intensified, and made subject to greater levels of surveillance, hierarchy, and precarity. In doing so, the authors also demonstrate the importance of combining political economy and Foucauldian approaches to neoliberalism, to highlight the way that external structural conditions and subjective processes combine to create new labour processes to which participants find themselves consenting and actively reproducing. JEL Codes: J5, L2

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)0.007
metaresearch head score (Gemma)0.008
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: none
Teacher disagreement score0.996
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.007
Science and technology studies0.0040.014
Scholarly communication0.0090.007
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.015
GPT teacher head0.260
Teacher spread0.246 · 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

Citations27
Published2021
Admission routes2
Has abstractyes

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