It Takes a Village: Candidate Recruitment in Alberta's Municipalities
Bibliographic record
Abstract
Many political scientists consider recruitment to be one of the most important stages in the candidate emergence process and is largely thought of as being a main function of political parties. This is in part because political parties want to attract and nominate high quality candidates, but also because for candidates, having that party backing is necessary for electoral success. However, in non-partisan politics where parties have no formal function, who recruits candidates into office? And is recruitment as important in the absence of these party barriers? Further, political parties, are thought to serve as gatekeepers to women’s emergence as a candidate. Despite this theory, women’s presence in non-partisan municipal politics in Canada is no greater than that of the partisan provincial and federal chambers. Given how important recruitment is theorized to be to an individual’s emergence as a candidate, what role does this process play in women’s persistent underrepresentation in non-partisan municipal politics? I answer these questions using two data sources: the annual Canadian Municipal Barometer survey that was fielded to councillors and mayors in municipalities in Canada with a population of 9,000 or greater, as well as interviews with candidates running for these positions in the 2021 Alberta municipal elections in the province’s ten largest cities. I find that candidates are most often recruited by personal connections or by current and former politicians and that recruitment is an especially pertinent step in the candidate emergence process for serious candidates. I further find that women are recruited less often and are more likely to be self-starters than men. This helps to explain the gender disparity we see in non-partisan politics. These findings build on the existing literature by suggesting that recruitment can and should be theorized in the non-partisan environment. Given that women’s presence is no greater in these non-partisan chambers, it also suggests that we should revisit the theories that argue political parties serve as gatekeepers to women’s representation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".