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Record W2342134016 · doi:10.26522/ssj.v9i2.1138

Scholarship as Cultural Production in the Neoliberal University: Working Within and Against ‘Deliverables’

2016· article· en· W2342134016 on OpenAlexaffvenueabout
Mary Elizabeth Luka, Alison Harvey, Mél Hogan, Tamara Shepherd, Andrea Zeffiro

Bibliographic record

VenueStudies in Social Justice · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsBrock UniversityToronto Metropolitan University
Fundersnot available
KeywordsScholarshipDeliverableDissentConversationContext (archaeology)SociologyPublic relationsPolitical sciencePoliticsMedia studiesManagementLawEconomics

Abstract

fetched live from OpenAlex

This article focuses on the idea of scholarly work as cultural production to help understand how the tensions of precarious, early-career academic employment are articulated on a day-to-day basis in the context of pressures to efficiently produce monetizable ‘deliverables.’ Using a political economy of communication framework and an iterative methodological approach, the authors mobilize examples drawn from a collaborative set of activities they undertook as part of a broader research group of emerging Canadian scholars working in different international contexts between 2012 and 2015. The research conversation began in academic roundtables in 2013, and was furthered through a content analysis of articles collected from scholarly and general interest blog posts, newsletters, and magazines published online from July 2012 to April 2014. In this article, the authors explore emerging themes and document pressures to conform to neoliberal practices within the corporatized university, as well as suggest pathways for dissent and reinvention of academic labour.

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)0.029
metaresearch head score (Gemma)0.027
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 score0.972
Threshold uncertainty score0.176

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.027
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.005
Science and technology studies0.0280.146
Scholarly communication0.0370.018
Open science0.0030.025
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0030.000

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 head0.081
GPT teacher head0.374
Teacher spread0.293 · 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

Citations22
Published2016
Admission routes3
Has abstractyes

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