The Local Determinants of Representation: Party Constituency Associations, Candidate Nomination and Gender
Bibliographic record
Abstract
Abstract It is well established that political parties play a key role as gatekeepers to elected office. This article explores the local determinants of a diverse candidate pool. In particular, we seek to uncover the district- or riding-specific party factors that are related to women's participation in the parties’ candidate nomination stages. That is, why do some nomination races in a party have no women contestants, while others have many? Using data from an original survey of party constituency association presidents, as well as extensive nomination data from Elections Canada, we demonstrate that a number of local factors are related to the presence of women contesting a party's nomination. Local party associations with a woman serving as president, as well as associations that hold earlier and longer nominations, are significantly more likely to see a woman enter the contest. The results are important since they call attention to what parties do at the grassroots level, as well as highlight practical solutions for parties seeking to have more diversity in their candidate pool.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".