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Record W1901162723 · doi:10.1111/joms.12166

To Frack or Not to Frack? The Interaction of Justification and Power in a Sustainability Controversy

2015· article· en· W1901162723 on OpenAlexaffabout
Jean‐Pascal Gond, Luciano Barin Cruz, Emmanuel Raufflet, Mathieu Charron

Bibliographic record

VenueJournal of Management Studies · 2015
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsUniversité LavalHEC Montréal
Fundersnot available
KeywordsLegitimacyPower (physics)Government (linguistics)PoliticsPosition (finance)SustainabilityLaw and economicsOutcome (game theory)Political economyEconomicsPolitical sciencePositive economicsBusinessSociologyLawMicroeconomics

Abstract

fetched live from OpenAlex

ABSTRACT How could a de facto moratorium on shale gas exploration emerge in Québec despite the broad adoption of fracking in North American jurisdictions, support from the provincial government and a favourable power position initially enjoyed by the oil and gas industry? This paper analyses this turn of events by studying how stakeholders from government, civil society, and industry mobilized modes of justification and forms of power with the aim to influence the moral legitimacy of the fracking technology during a controversy surrounding shale gas exploration. Combining Boltanski and Thévenot's economies of worth theory with Lukes’ concept of power, we analytically induced the justification of power mechanisms whereby uses of power become justified or ‘escape’ justification, and the power of justification mechanisms by which justifications alter subsequent power dynamics. We finally explain how these mechanisms contribute to explaining the controversy's ultimate outcome, and advance current debates on political corporate social responsibility.

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)0.013
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.989
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0110.063
Scholarly communication0.0100.007
Open science0.0010.004
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.034
GPT teacher head0.318
Teacher spread0.284 · 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.

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

Citations97
Published2015
Admission routes2
Has abstractyes

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