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Record W2087346960 · doi:10.1287/orsc.2014.0918

The Coevolution of Industries, Social Movements, and Institutions: Wind Power in the United States

2014· article· en· W2087346960 on OpenAlexaff
Desirée F. Pacheco, Jeffrey G. York, Timothy J. Hargrave

Bibliographic record

VenueOrganization Science · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Acceptance of Renewable Energy
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsDiversity (politics)ConceptualizationProcess (computing)CoevolutionEconomic geographyIndustrial organizationBusinessSocial movementEconomicsPolitical scienceComputer scienceEcologyPolitics

Abstract

fetched live from OpenAlex

This study of the U.S. wind energy industry extends theory on the process of industry emergence by developing and testing a coevolutionary model of the relationship between social movement organizations (SMOs), institutions, and industries. Building on research that suggests that SMOs can influence institutions and the path of emerging industries, we show that the growth of an industry can also influence the diversity of social movements by motivating the participation of specialist SMOs. These new SMOs in turn deploy distinct knowledge, capabilities, goals, and strategies to produce institutional changes that are necessary for the continued growth of the industry. Our study offers a more complete conceptualization of the influence of social movements on industry emergence and growth, and it extends understanding of how SMO diversity is produced.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.045
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.293
Teacher spread0.271 · 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

Citations138
Published2014
Admission routes1
Has abstractyes

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