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Record W4413988262 · doi:10.1017/s0008423925100553

The Political Character of Agency and Board Appointments in Alberta

2025· article· en· W4413988262 on OpenAlexafffundabout
Carey Doberstein, Katelynn Kowalchuk, Kael Kropp

Bibliographic record

VenueCanadian Journal of Political Science · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Policy and Administration Research
Canadian institutionsMcGill UniversityUniversity of British ColumbiaUniversity of British Columbia Hospital
FundersMcGill University
KeywordsCharacter (mathematics)PoliticsAgency (philosophy)Public administrationPolitical scienceBusinessSociologyLawSocial scienceMathematics

Abstract

fetched live from OpenAlex

Abstract Agencies, boards, and commissions (ABCs) in Canada have a distinct character and set of governance dynamics compared with the conventional public service. ABCs are often conceived to deliver a particular service or regulate or adjudicate matters with some distance from the government of the day, yet (perhaps counter-intuitively) are among the few remaining sites of patronage-like appointments in Canada. This article compiles ABC appointment data ( N = 2,248) from the Province of Alberta over two distinct periods—the Notley government (2015–2019) and the Kenney/Smith governments (2019–2024)—to explore the character and patterns of appointments. We find mixed evidence that appointments to ABCs with more formal autonomy are more likely to be politicized. Some metrics also suggest that the conservative party appointees are slightly more often politically connected, particularly in ABCs that reside in high priority policy areas for the appointing government, and in particular in crown corporations and regulatory agencies.

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)0.002
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.955
Threshold uncertainty score0.326

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.007
Science and technology studies0.0050.003
Scholarly communication0.0040.001
Open science0.0010.002
Research integrity0.0010.001
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 head0.028
GPT teacher head0.385
Teacher spread0.356 · 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 designNot applicable
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
Published2025
Admission routes3
Has abstractyes

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