Printed in Great Britain 0261-3794/98 $19.0010.00 PII: S0261-3794(98)00020-1 A Leadership Selection Index for Multi-Ballot Political Party Conventions in Canada
Bibliographic record
Abstract
multi-ballot leadership conventions in the United States. He hypothesized that the change in absolute votes between the first and the second ballot could be used to forecast the eventual winner of a leadership race. When the McGregor model is applied to the Canadian multi-ballot conventions the prediction rate is only 57 per cent. This paper seeks first to discuss the qualitative similarities found between these Canadian conventions and then use this as a basis to develop and to apply a model that extends on McGregor’s work. The developed model is called the Leadership Selection Index (LSI). We find that the winner of most Canadian multi-ballot conven-tions (86%) could be forecasted after the second ballot by using the LSI method of analysis. Based on the evidence from the literature and a survey of Canadian political conventions, we develop a hypothesis of ‘two critical conjunctures ’ for multi-ballot leadership conventions. This hypothesis provides a suitable theoretical framework to
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.809 | 0.514 |
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".