Bibliographic record
Abstract
Abstract For the case of elections since 1988, this chapter looks at the suggestion made by Andre Siegfried in 1907 that Canadian elections are peculiarly vulnerable to leader effects. It begins by outlining the specifically Canadian arguments for taking leadership seriously and the evidence said to back them; the arguments are found to refer mainly to ‘indirect’ effects in the sense used by King in Chapter 1 (so that leaders are treated as embodied preferences), with the actual evidence largely silent on the content – personality or otherwise – of Canadian judgments on leaders, at least in so far as those judgments are linked to the vote. Likewise, most accounts control for competing explanations weakly, if at all, and none considers personality for its net, election–day effect. Working through each argument also reveals that each is highly contingent, generally applying more to certain parties or party sizes than others, and more to periods of flux and to new parties than to stable periods and old parties. Filling the gaps requires an account of the personality factors worth taking seriously (the analysis looks at competence and character), a basic estimation strategy setting these attributes into proper context (which is given), and based on this estimation strategy, an accounting for net aggregate effects (also given). The last two sections of the chapter discuss whether perceptions of personality can be modified over the course of a campaign, and give an account of a special sort of ‘indirect’ effect: the case where perceptions of a leader’s personality can be cashed in on perceptions of a policy option (here the proposal for a commercial union between Canada and the United States in 1988).
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.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.014 | 0.002 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 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".