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Record W4409112281 · doi:10.1017/s0008423924000805

Incentives to Join a Local Party Association: Evidence from Canada

2025· article· en· W4409112281 on OpenAlexaffabout
Scott Pruysers

Bibliographic record

VenueCanadian Journal of Political Science · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicElectoral Systems and Political Participation
Canadian institutionsDalhousie University
Fundersnot available
KeywordsJoin (topology)IncentiveAssociation (psychology)BusinessPolitical sciencePublic administrationEconomicsMicroeconomicsPsychology

Abstract

fetched live from OpenAlex

Abstract The comparative literature has devoted considerable attention to why individuals join political parties. This is especially important in the context of the declining party membership and activism that political parties face in contemporary politics. While the question of why members join parties has been well-documented, considerably less work has considered incentives to join other party positions. In the Canadian case, for example, we know very little about the incentives to join an electoral district association (EDA). This is surprising given the consequential role—both formal and informal—that local party associations and their presidents have been known to play in intra-party politics (influencing candidate nomination, membership recruitment and so forth). This study applies Clark and Wilson's (1961) framework of material, solidary and purposive incentives to local party association membership and asks why individuals join their local party executive and whether this motivation shapes the subsequent character of the EDA.

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)0.021
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 score0.047
Threshold uncertainty score0.338

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.008
Science and technology studies0.0070.003
Scholarly communication0.0040.001
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0120.001

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 head0.035
GPT teacher head0.342
Teacher spread0.307 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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