Training Local Elected Officials: Professionalization Amid Tensions Between
Bibliographic record
Abstract
Over the last few decades, several administrations in Canada have organized programs for training local elected officials (LEOs). While improving LEOs’ competences is beneficial, this trend is developing amidst a persisting tension between democratic and technocratic approaches to governance. Indeed, training - and the professionalization it entails - disrupts the enduring principle holding that everyone is equally authorized to govern following the democratic election. Despite the significance of these transformations, training activities for LEOs have received limited scholarly attention until now. In this paper, we detail our conceptualization of the professionalization process and the role of training programs within it. We then review the existing Canadian training programs for LEOs. We also examine one case study: the main introductory training program for LEOs in Québec (Canada) since 2011. Accordingly, we advance our understanding of training’s effects on elected officials by emphasizing how they contribute to a long-term process of professionalization.
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.023 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".