Exploring How Canadian Voters Evaluate Leader Character in Three Cases: Justin Trudeau, Hillary Clinton, and Donald Trump
Bibliographic record
Abstract
In exploratory research, we investigate whether a recently developed framework of leader character, grounded in the business administration literature, has any utility for understanding how citizens value the character of modern political leaders. We are interested in whether the entire leader character framework, or only a subset of its dimensions, are valued by Canadians in political leaders. An opinion poll of 506 Canadians in the fall of 2016 examines how they responded to the framework, which dimensions of leader character they value highly, and how they employed it to evaluate three well-known politicians who were then in the media spotlight: Canadian prime minister Justin Trudeau; and American presidential candidates Hillary Clinton and Donald Trump. The results suggest Canadians possess a clear, distinct set of preferences with respect to the ideal shape of leader character. This finding is salient toward understanding the modern political culture of Canada, as well as addressing speculation that the rise of populism in many countries suggests voters might embrace a leader in the mould of American president Donald Trump.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.026 | 0.006 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".