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Record W2013980682 · doi:10.5465/amr.2008.31193554

Making Friends in Hostile Environments: Political Strategy in Regulated Industries

2008· article· en· W2013980682 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAcademy of Management Review · 2008
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPolitical Influence and Corporate Strategies
Canadian institutionsWestern University
Fundersnot available
KeywordsPoliticsAgency (philosophy)Government (linguistics)Set (abstract data type)EconomicsControl (management)BusinessIndustrial organizationPolitical economyPublic relationsMarketingPublic economicsEconomic systemPolitical scienceManagementSociologyLawComputer science

Abstract

fetched live from OpenAlex

We examine how regulated firms target their political strategies at multiple government institutions to gain more favorable regulatory agency decisions. Integrating the corporate political strategy literature and positive political theory literature, we derive propositions that (1) identify the political and regulatory circumstances generating hostile environments and (2) delineate the conditions under which firms will employ an indirect strategy instead of a direct strategy to induce changes in regulator decisions, identifying the specific political institutions a firm will target when adopting an indirect strategy. We develop a rich set of predictions about firms' political strategy that can form the basis for future empirical testing.

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.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.889
Threshold uncertainty score0.942

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.100
GPT teacher head0.314
Teacher spread0.214 · 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