Do Leaders' Personalities Really Matter?
Bibliographic record
Abstract
Abstract This is an introductory chapter, which starts with a general discussion of whether leaders’ personalities really matter in determining the outcome of democratic elections, and then considers a number of preliminary points before the country analyses are presented in the following six chapters. The first point is to differentiate between the indirect influences a leader can have on voters and an election outcome (via his influence on his political party or government or administration) and the direct influence of a candidate’s personality or personal characteristics; this book is about the latter. The second point is to define what is meant by ‘personality or personal characteristics’, and the next two points are a discussion of why leaders’ attributes might, or might not, be thought to matter. The fifth point is to suggest analytical strategies for disentangling the effects of leaders’ personalities or personal characteristics from other factors; the three advanced are the experimental, improved–prediction and counterfactual strategies. Next, previous analytical findings are presented for the six countries studied in the book (United States, Britain, France, Germany, Canada and Russia), and finally, hypotheses are offered for explaining when the impact of candidates’ personalities or personal characteristics might be greatest.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".