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Record W2531750939 · doi:10.5465/ambpp.2016.63

David Defeating Goliath: Institutional Work of A Marginalized Actor Within Institutional Complexity

2016· article· en· W2531750939 on OpenAlexaffabout
Sofiane Baba, Innan Sasaki

Bibliographic record

VenueAcademy of Management Proceedings · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Theory and Institutions
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsConceptualizationNegotiationInstitutional logicWork (physics)Unintended consequencesField (mathematics)Power (physics)Institutional theoryInstitutional changeIndigenousSociologyPolitical sciencePolitical economyPublic administrationSocial scienceLawComputer scienceEngineering

Abstract

fetched live from OpenAlex

The purpose of our study is to answer the following research question: how can marginal actors with limited resources and power achieve institutional change by creating and promoting their logic? Drawing on a historical-longitudinal case study of one of the most intense environmental controversy between an Indigenous Nation, and Hydro-Québec, in James Bay, northern Quebec, this paper makes three contributions to current discussions of institutional change and logics. First, we identify three strategic mechanisms - capacity-building, field expansion and changing own logic - through which marginalized actors are able to initiate and maintain the need for change despite continuous pressure from a strong, resourceful and legitimate competing logic. Second, responding to several calls for research on the unintended outcomes of the institutional work achieved, our research advances the conceptualization of institutional work accumulation. Third, our study shows that actors and the logics they convey are not static; rather, they are dynamic and evolve during the process while actors negotiate issues of competing logics and complexity.

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)0.001
metaresearch head score (Gemma)0.000
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.312
Threshold uncertainty score0.735

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.085
GPT teacher head0.252
Teacher spread0.167 · 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 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".

Quick stats

Citations0
Published2016
Admission routes2
Has abstractyes

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