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Record W3193911098 · doi:10.1111/joms.12765

Under the Radar: Institutional Drift and Non‐Strategic Institutional Change

2021· article· en· W3193911098 on OpenAlexaff
Maxim Voronov, Mary Ann Glynn, Klaus Weber

Bibliographic record

VenueJournal of Management Studies · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsYork University
Fundersnot available
KeywordsEthosInstitutional changeOrder (exchange)Institutional theoryReflexivityPerspective (graphical)Interpersonal communicationNew institutionalismInstitutionalismSociologyPositive economicsPolitical scienceBusinessEconomicsPublic administrationSocial scienceComputer science

Abstract

fetched live from OpenAlex

Abstract Although researchers have acknowledged that not all institutional change results from the intentional efforts of relatively reflexive actors, we lack an explanation of how mundane interactions between actors can result in non‐strategic institutional change. To address this, we advance the theory of institutional drift that reveals how the practice deviation(s) that occur between interaction partners in an institutional order, transformed into tolerable deviations by the self and others, can lead to the non‐strategic transformation of that institutional order. Our framework extends the interactionist perspective in organizational institutionalism by showing how interpersonal interactions are animated and constrained by people’s passionate attachment to the fundamental sacred ideals, or ethos, underlying institutional orders. It is this connection with ethos that animates the interactional processes tied to both maintaining and disrupting institutions.

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)0.017
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0040.035
Scholarly communication0.0080.012
Open science0.0010.010
Research integrity0.0020.004
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 head0.063
GPT teacher head0.259
Teacher spread0.196 · 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 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

Citations28
Published2021
Admission routes1
Has abstractyes

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