Do Leaders' Personalities Really Matter?
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.053 | 0.006 |
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