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Record W2750355239 · doi:10.1177/1350508417713218

Supporting alternative organizations? Exploring scholars’ involvement in the performativity of worker-recuperated enterprises

2017· article· en· W2750355239 on OpenAlexaff
Susana Esper, Laure Cabantous, Luciano Barin‐Cruz, Jean‐Pascal Gond

Bibliographic record

VenueOrganization · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Economic Solidarity
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsPerformativityConceptualizationCritical management studiesSociologyPerformative utteranceOrganization studiesEpistemologySocial scienceManagementEconomicsGender studies

Abstract

fetched live from OpenAlex

This article analyses the role of academics in the production and maintenance of alternative organizations within the capitalist system. Empirically, we focus on academics from the University of Buenos Aires who, through the extension programme Facultad Abierta, have supported worker-recuperated enterprises since their emergence in Argentina in the early 2000s. Conceptually, we build on prior studies on worker-recuperated enterprises as well as the ‘critical performativity’ concept that we define as scholars’ subversive interventions that can involve the production of new subjectivities, the constitution of new organizational models and/or the bridging of these models to current social movements. Our results uncover the multiple roles of academics in relation to these three facets and highlight the key interactions of these roles. In so doing, our analysis advances prior studies of worker-recuperated enterprises by clarifying how academics can support alternative organizations while offering a renewed conceptualization of critical performativity as a multifaceted process through which academics and workers interact.

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)0.020
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0120.037
Scholarly communication0.0130.007
Open science0.0020.014
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.070
GPT teacher head0.334
Teacher spread0.265 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations47
Published2017
Admission routes1
Has abstractyes

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