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The Path of Least Resistance: The historical contingency of intra-logic persistence and change

2012· article· en· W2901341153 on OpenAlexaff
Mia Raynard, Farah Kodeih

Bibliographic record

VenueAcademy of Management Proceedings · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPersistence (discontinuity)Path dependenceInstitutional logicContingencyPath (computing)TRACE (psycholinguistics)Ideal (ethics)Institutional changeComputer scienceResistance (ecology)Contingency theoryTrajectoryEconomic geographyEpistemologyPolitical scienceSociologyEconomicsMicroeconomicsKnowledge managementEngineeringSocial science

Abstract

fetched live from OpenAlex

This study examines the how a locally instantiated logic emerges and changes over time. Employing a longitudinal cross-level research design, we trace the evolution of French Grandes Écoles of Commerce (FGEC) from the early 1800s to the present. Our findings reveal that changes in the broader institutional environment trigger critical junctures, whereby organizations are confronted with a heightened level of institutional complexity. Organizational responses to complexity during these critical junctures not only act to recursively change an incumbent logic, but also influence the future trajectory of intra-logic evolution. We develop an analytical framework outlining six ideal-type mechanisms and processes that characterize intra-logic persistence and change. These ideal types can be organized along three broad dimensions: instrumental, structural, and cultural-inertial. This study empirical demonstrates that intra-logic change is bound by past decisions, path-dependent processes, and a supporting institutional infrastructure that is reproduced and perpetuated by self-reinforcing mechanisms.

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)0.005
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0030.016
Scholarly communication0.0050.006
Open science0.0010.003
Research integrity0.0010.002
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 head0.043
GPT teacher head0.225
Teacher spread0.183 · 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 designTheoretical or conceptual
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".

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Citations0
Published2012
Admission routes1
Has abstractyes

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