Try, Try Again? Are Unsuccessful Leadership Contestants Sore Losers?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Elections, even intra-party ones, create winners and losers. A number of recent studies have revealed a ‘sore losers’ effect among a number of party actors. The evidence suggests that those who support a losing candidate in an internal party election are significantly less likely to remain active and involved in party politics compared to those who supported the winner. Much less, however, is known about the losing candidates themselves. This paper explores whether losing leadership candidates also exhibit a ‘sore losers’ tendency. Drawing on an original dataset of unsuccessful leadership contestants in three Canadian parties, results reveal that losing leadership candidates do not exit their party en masse but rather they remain generally committed to their party, often seeking re-election during the next general election. The results provide important insight into the behaviour of leadership candidates and provide nuance to the sore losers debate by examining an understudied cohort of party actors: the losing candidates themselves.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it