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Record W2335202686 · doi:10.1177/0170840615613372

The Enabling and Constraining Effects of Social Ties in the Process of Institutional Entrepreneurship

2016· article· en· W2335202686 on OpenAlexaff
Israr Qureshi, Geoffrey M. Kistruck, Babita Bhatt

Bibliographic record

VenueOrganization Studies · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsYork University
Fundersnot available
KeywordsInterpersonal tiesEntrepreneurshipPerspective (graphical)SociologyProcess (computing)Norm (philosophy)Economic geographyPositive economicsPublic relationsSocial psychologyPolitical scienceSocial sciencePsychologyEconomics

Abstract

fetched live from OpenAlex

While the past decade has produced a number of insights into the process of institutional change, scholars still lack a comprehensive understanding of the germinal stages of institutional entrepreneurship. More specifically, further knowledge is needed into what factors cause certain individuals to initiate norm-breaking behaviour while others continue to adhere to societal expectations. Prior work seeking to inform this question has focused either on individual-level or environmental-level explanations. Comparatively, we employ a social network perspective as a ‘meso-level’ lens into the space where actors and their environment intersect. Based upon our qualitative findings, we propose that social ties can serve as an important factor in enabling (heterophilic ties) as well as constraining (homophilic ties) institutional change. However, our data also suggest that these network forces are highly dynamic and contingent upon tie frequency, the sequencing of tie contact, and the prevailing social norms in which tie contact takes place.

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)0.001
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 score0.437
Threshold uncertainty score0.328

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.017
GPT teacher head0.240
Teacher spread0.224 · 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

Citations116
Published2016
Admission routes1
Has abstractyes

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