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Record W7029195286

Interests, information and influence: a comparative analysis of interest group influence in the European Union

2011· dissertation· en· W7029195286 on OpenAlexfundno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2011
Typedissertation
Languageen
FieldBusiness, Management and Accounting
TopicPolitical Influence and Corporate Strategies
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Edinburgh
KeywordsInterest groupEuropean unionSpecial Interest GroupOrder (exchange)Set (abstract data type)Interest ratePublic interestProcess (computing)
DOInot available

Abstract

fetched live from OpenAlex

Faulty assumptions about the nature of interest group activity have misled scholars' assessments of interest group influence in the European Union (EU).The influence literature portrays interest groups as commonly using undue pressure and purchase tactics in order to change the minds of decision-makers.However, this work on influence has yet to take seriously insights from the rest of the interest group literature, which has long established that interest groups are much more likely to lobby decisionmakers who already share their views (friends) rather than to attempt to change the minds of those who do not (foes).Moreover, in lobbying friends, interest groups are best understood as informational service bureaus, providing policy-relevant information to decision-makers in exchange for legitimate access to the policy-making process.This dissertation brings these insights to bear on interest group influence in the EU.I conceive of interest group influence as a function of an interest group's ability to efficiently and reliably provide policy-relevant information to EU decision-makers.To this end, I examine the information processing capacity -how interest groups gather, filter, make sense of, generate and transmit information -of EU interest groups within a comparative framework.I find that, in general, interest group influence in the EU is balanced with no particular set of groups dominating the EU policy-making process at the expense of others. RésuméDes hypothèses erronées quant à la nature de l'activité des groupes d'intérêt ont induit en erreur plusieurs experts dans leur analyse de l'influence de ces groupes au sein de l'Union européenne (UE).Leur travaux sur l'influence des groupes de pression affirment que les groupes d'intérêt recourent couramment à une pression inutile et à des techniques de vente dans le but de faire changer d'avis les décideurs politiques.Toutefois, cette lecture de l'influence de ces groupes n'a pas su intégrer les conclusions du reste des travaux scientifiques sur les groupes d'intérêt qui affirment depuis longtemps que ces groupes d'intérêt sont bien davantage susceptibles de faire pression sur des décideurs qui partagent déjà leur point de vue (alliés) que d'essayer de faire changer d'avis ceux qui ne le partagent pas (adversaires).De plus, dans le cercle des lobbyistes, les groupes d'intérêt sont davantage perçus comme des bureaux de renseignements, qui fournissent des informations pertinentes d'un point de vue politique aux décideurs en échange d'un accès légitime au processus décisionnel.Ce projet de recherche s'attarde à illustrer les principes qui sous-tendent l'influence des groupes d'intérêt dans l'Union européenne.Je considère l'influence des groupes d'intérêt comme fonction de leur capacité à fournir de manière efficace et fiable des informations pertinentes d'un point de vue politique aux décideurs de l'UE.À cet effet, j'examine la capacité des groupes d'intérêt au sein de l'UE à traiter l'information, à savoir comment ces groupes rassemblent, sélectionnent, analysent, génèrent et transmettent l'information, dans une analyse comparative.J'estime qu'en général, l'influence des groupes d'intérêt de l'UE n'est pas caractérisée par la présence de certains groupes en particulier qui domineraient le processus décisionnel européen au détriment d'autres groupes.I am very much indebted my wonderful supervisor, Juliet Johnson, who offered guidance, encouragement and her keen insight every step of the way.I also would like to thank Hudson Meadwell and Maria Popova for their comments and support at various stages of the dissertation process.

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)0.007
metaresearch head score (Gemma)0.021
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 score0.011
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0060.009
Science and technology studies0.0020.003
Scholarly communication0.0050.005
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.048
GPT teacher head0.265
Teacher spread0.217 · 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

Citations0
Published2011
Admission routes1
Has abstractyes

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