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Record W1978506782 · doi:10.1287/orsc.2014.0959

Filtering Institutional Logics: Community Logic Variation and Differential Responses to the Institutional Complexity of Toxic Waste

2015· article· en· W1978506782 on OpenAlexaff
Min‐Dong Paul Lee, Michael Lounsbury

Bibliographic record

VenueOrganization Science · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Policy and Administration Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsInstitutional logicInstitutional theoryField (mathematics)Organizational ecologyCompetition (biology)Organizational fieldSociologyBusinessPublic relationsPolitical scienceSocial scienceMathematics

Abstract

fetched live from OpenAlex

Although many recent studies have emphasized the multiplicity of institutional logics and the competition among them, how some institutional logics become prioritized over others in shaping organizational decisions is undertheorized. Drawing on panel data of 118 industrial facilities across 34 communities in Texas and Louisiana, we show that the saliency of different kinds of community logics significantly affects environmental practices—specifically, toxic waste emissions—of facilities in a community. Our results show that community logics not only have direct effects but also have indirect effects by filtering organizational reactions to broader field-level institutional logics. We theorize how community logics can amplify or dampen the influence of broader field-level logics and discuss the implications for the study of institutional complexity, social movements, and values in the configuration of institutional logics.

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)0.006
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.033
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.003
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
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 head0.265
GPT teacher head0.412
Teacher spread0.147 · 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 designQualitative
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

Citations320
Published2015
Admission routes1
Has abstractyes

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