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Record W2979382507 · doi:10.1177/0170840619874462

Proudly Elitist and Undemocratic? The distributed maintenance of contested practices

2019· article· en· W2979382507 on OpenAlexaff
Mia Raynard, Farah Kodeih, Royston Greenwood

Bibliographic record

VenueOrganization Studies · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsEmbeddednessOrchestrationLegitimacyEliteImprovisationFlexibility (engineering)Extant taxonSociologyInstitutional theoryField (mathematics)Public relationsPolitical economyPolitical scienceManagementEconomicsPoliticsSocial science

Abstract

fetched live from OpenAlex

This study examines the maintenance of highly institutionalized practices during periods of vehement contestation and changing external demands. Employing a cross-level longitudinal research design, we explore how the recruitment model of elite French business schools persisted, remaining fundamentally intact despite serious questions raised about its functional utility and social legitimacy. Comparing three periods of contestation, we document shifting coalitions of dispersed actors that were incentivized to “thematically” maintain the practices in the focal field with little formal orchestration. Our findings indicate that practices which contribute to social stratification often foster meta-routines that cajole constituencies in multiple fields to, collectively and self-interestedly, promote and regulate conservative change. We identify three meta-routines—referential comparison, generative improvisation, and distributed monitoring and policing—that introduced flexibility and encouraged “unforced” adaptations. In elaborating these meta-routines, we contribute to extant theory on the mechanisms of institutional maintenance, and shed further light on the role of complex embeddedness as a constraint on institutional processes.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.164
Threshold uncertainty score0.324

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.023
GPT teacher head0.249
Teacher spread0.226 · 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 teacher head, not a consensus.

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

Citations35
Published2019
Admission routes1
Has abstractyes

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