8 Conclusions and Implications
Bibliographic record
Abstract
Abstract The focus of this book has been on the impact that leaders’ personalities and other personal traits may have had from time to time, not on their parties or governments, but on individual voters’ willingness to vote for them and consequently on the outcomes of the elections that they contest. The line of argument usually runs: (1) voters have likes and dislikes of leaders and candidates; (2) on the basis of those likes and dislikes, voters form overall evaluations of leaders and candidates; (3) voters’ overall evaluations of leaders and candidates have a considerable bearing – perhaps a decisive bearing – on how they actually vote. The argument then usually continues (4) because voters’ overall evaluations of leaders and candidates have a considerable bearing on the votes of individuals, they also, therefore, often have a bearing on the outcomes of whole elections; arguments (1) and (2) are not disputed, but arguments (3) and (4) are. A table is presented setting out the editor’s best estimates of which elections over the past four decades in each of the six countries studied (United States, Britain, France, Germany, Canada and Russia) have, and have not been, decided by voters’ comparative evaluations of the main political parties’ candidates; these estimates are discussed with respect to each country, and various conclusions drawn. Overall, the table suggests that there are some elections in which the leaders’ and candidates’ personalities have proved decisive, and the distinguishing features of these elections are discussed. However, the core finding of the book is that personality factors determine election outcomes far less often then is usually supposed.
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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 | 0.007 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.133 | 0.027 |
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".