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Record W4386791743 · doi:10.1080/01900692.2023.2256489

Do Parliamentary Roles Affect Lobbying Activities? Evidence from the Canadian House of Commons

2023· article· en· W4386791743 on OpenAlexaffabout
Maxime Boucher, Alex Marland

Bibliographic record

VenueInternational Journal of Public Administration · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPolitical Influence and Corporate Strategies
Canadian institutionsAcadia UniversityUniversity of OttawaInstitute on Governance
Fundersnot available
KeywordsParliamentHouse of CommonsCabinet (room)Parliamentary procedureLegislatureLawPolitical sciencePublic administrationSelect committeeGovernment (linguistics)CommonsPolitics

Abstract

fetched live from OpenAlex

In parliamentary systems, private members (i.e. backbenchers) with formal titles and roles can affect the institutional system in which politicians, civil servants and interest groups are embedded. Packing legislative institutions with backbenchers who act as agents of the government but who are not in Cabinet puts certain Members of Parliament in a privileged position with the core executive. We hypothesize that influential positions in Canada’s House of Commons, notably a parliamentary secretary tasked with supporting a minister or a chair of a parliamentary committee, bring increased external pressure from interest group lobbyists. We test these assumptions with data on communications between MPs and interest group lobbyists gathered from the federal Registry of Lobbyists and open data lists found on the website of the Parliament of Canada. Our results show that a parliamentary secretary position or a seat on a standard committee exposes MPs to higher lobbying volumes.

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)0.004
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.964
Threshold uncertainty score0.265

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.034
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.006
Science and technology studies0.0060.004
Scholarly communication0.0050.002
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0140.001

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 head0.096
GPT teacher head0.325
Teacher spread0.229 · 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 designObservational
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

Citations1
Published2023
Admission routes2
Has abstractyes

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