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Record W4387519818 · doi:10.1111/1475-6765.12630

Fight or flight: How access barriers and interest disruption affect the activities of interest organizations

2023· article· en· W4387519818 on OpenAlexfundno aff
Wiebke Marie Junk, Michele Crepaz, Ellis Aizenberg

Bibliographic record

VenueEuropean Journal of Political Research · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPolitical Influence and Corporate Strategies
Canadian institutionsnot available
FundersQueen's UniversityUniversiteit LeidenQueen's University Belfast
KeywordsAffect (linguistics)BusinessPublic relationsPolitical sciencePsychologyCommunication

Abstract

fetched live from OpenAlex

Abstract Central theories of public policy imply that lobbying is demand‐driven, meaning highly responsive to the levels of access that political gatekeepers offer to interest organizations. Others stress drivers at the supply side, especially the severity of disturbances which affect an organization's constituency. We test these central arguments explaining lobbying activities in a comparative survey experiment conducted in 10 polities in Europe. Our treatments vary the severity of two types of external threats faced by interest organizations: (1) barriers that restrict their access to decision‐makers and (2) disturbances that compromise an organization's interests. We operationalize these threats at the demand and supply side of lobbying based on an (at that point) hypothetical second wave of COVID‐19. Our findings show that while severe access barriers trigger a flight response, whereby groups suspend their lobbying activities and divert to protest actions, higher disturbances mobilize groups into a fight mode, in which organizations spend more lobbying resources and intensify different outside lobbying activities. Our study serves novel causal evidence on the important dynamic relationship between policy disturbances, political access and lobbying strategies.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.776
Threshold uncertainty score0.932

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.275
GPT teacher head0.406
Teacher spread0.131 · 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 designTheoretical or conceptual
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

Citations9
Published2023
Admission routes1
Has abstractyes

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