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Record W2898274783 · doi:10.1111/capa.12294

Who you know in the PMO: Lobbying the Prime Minister's Office in Canada

2018· article· en· W2898274783 on OpenAlexaboutno aff
Maxime Boucher

Bibliographic record

VenueCanadian Public Administration · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPolitical Influence and Corporate Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsPrime ministerScope (computer science)Prime (order theory)Government (linguistics)Public administrationExecutive branchPolitical sciencePublic relationsBusinessPublic economicsPoliticsEconomicsLaw

Abstract

fetched live from OpenAlex

Abstract This article examines the relationship between lobbying organizations, the Prime Minister’s Office (PMO), and other central agencies of the Canadian government. First, an overall examination of the Canadian lobbying registry shows that the PMO is one of the most lobbied institutions of the executive branch. Second, a statistical model evaluates the effect of organizational (such as the interest type and policy sector) and other strategic factors on the volume of communication between lobbying organizations and the PMO. This inquiry concludes that strategic choices related to the amount of lobbying activities and the scope of lobbying campaigns have the most consistent impact on the access to top‐level policymakers. In the end, the results support the claim that lobbying should be understood as a social, self‐reinforcing process unfolding over the medium‐ and long‐term.

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)0.003
metaresearch head score (Gemma)0.015
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 score0.900
Threshold uncertainty score0.723

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0150.006
Scholarly communication0.0060.001
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0070.000

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 head0.029
GPT teacher head0.230
Teacher spread0.202 · 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

Citations17
Published2018
Admission routes1
Has abstractyes

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