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Record W2104219683 · doi:10.1287/orsc.2014.0902

The Erosion of Expert Control Through Censure Episodes

2014· article· en· W2104219683 on OpenAlex
Ruthanne Huising

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueOrganization Science · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsNegotiationControl (management)Agency (philosophy)Relation (database)Work (physics)Process (computing)Public relationsGovernment (linguistics)Knowledge managementSociologyEthnographyBusinessEpistemologyComputer sciencePolitical scienceEngineeringArtificial intelligenceSocial science

Abstract

fetched live from OpenAlex

Organizations depend on experts to oversee and execute complex tasks. When faced with pressures to reduce their dependence on experts, managers encounter a control paradox: they require experts to explicate the very knowledge and discretionary approaches that are the basis of their control for the purpose of undercutting this control. Experts rarely consent to such a situation; therefore, attempts to reduce dependence on experts and control their work are more often aspirational than actual. Drawing on an ethnography of an organization that was required by a government agency to transfer the work responsibilities of experts to employees throughout the organization, this paper describes how a network of actors developed a discursive, political process to renegotiate control of expert work practices. Through censure episodes, long-standing and largely successful expert practices were examined one by one and relabeled as problematic in relation to established goals. The constructed breaches opened expert practices to evaluation, questioning, and eventual delegitimation within the organization. This process depended on the introduction of new roles that revised dependencies and generated new resources. This paper contributes to the understanding of control in organizations by theorizing how the emergent, symbolic work of censure episodes are a means of gradually subverting expert control. Further, these struggles are reconceptualized as multiple-role negotiations rather than bilateral manager–expert struggles.

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.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.917
Threshold uncertainty score0.774

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
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 head0.010
GPT teacher head0.220
Teacher spread0.209 · 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