Bibliographic record
Abstract
This chapter argues that building a theory of strong or weak mayoral leadership in the United States and Canada poses many challenges. But there is no reason why a theory of mayoral leadership has to be specific to the national context. Institutional structures may be different in the two countries, attitudes about government may vary, but mayoral leadership in both countries is about establishing a general policy or problem narrative, influencing senior levels of government and crisis management, and usually about proposing projects rather than unilaterally promulgating. For students of mayoral leadership, national affiliation is not the main dividing line, but rather how we think about leadership as a concept, regardless of country. There are marked differences concerning how such terms as “strong mayor,” “power,” and “leadership” should be understood. Determining the relative weight of such variables as personality, political culture, interests, and institutions is also a complicated matter, and there remains considerable confusion over research methods and appropriate questions, not to mention the difficulty of sorting out facts from groundless popular assumptions. The literature on mayoral leadership is much more extensive in the United States than it is in Canada. But the task of building a Canadian literature, almost literally from scratch in many instances, also affords an opportunity: to study mayoral leadership as a pre-emptive exercise—that is, narrowing or defining an agenda, instead of actually wielding formal authority or necessarily creating big projects.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.018 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".