Narcissistic Women and Cash-Strapped Men: Who Can Be Encouraged to Consider Running for Political Office, and Who Should Do the Encouraging?
Bibliographic record
Abstract
This paper not only considers whether encouragement can be an effective tool for increasing political ambition, but it also asks whether the source of that encouragement matters. That is, are some sources of encouragement more credible and effective than others? In addition, it explores the profiles of those individuals who are most likely to be receptive to recruitment, accounting for factors such as age, gender, income, education, political interest, knowledge, and personality. To answer these questions, we conducted two studies. The first is a survey of eligible voters. We recruited 371 Canadians from a national panel, asking a variety of questions regarding their level of political ambition. Importantly, we uncover distinct profiles for men and women who are most likely to respond positively to encouragement. In the second study, we conducted an online experiment with 443 undergraduate university students. Here, we focus on the question of who is providing the encouragement as we manipulated the gender of the actor providing the encouragement to run for office. We find that women who are encouraged by a male party recruiter are significantly less likely to express interest in a political career than those in our gender-neutral control condition.
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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 | 0.002 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".