“I’ve Got to Run Again”: Experiences of Social Workers Seeking Municipal Office in Ontario
Bibliographic record
Abstract
This thesis presents a qualitative study on the experiences and perceptions of Ontario social workers who were candidates for municipal elected office in the 2018 Ontario municipal elections. The author sought to understand the contributing factors these social workers perceive led to them seeking elected office, whether social justice was a motivating factor, and whether these social workers believe that their social work education prepared them for seeking elected office. I interviewed ten social workers and used thematic analysis, grounded in feminist theories and Verba et al.’s (1995) Civic Voluntarism Model to analyze transcripts. Participants discussed determining relationships, becoming a social worker, catalysts, the political landscape, skills and strategies, and deepening the political identity of social work. Discussion identified numerous factors that participants perceive as contributing to their political journeys, with emphasis on relationships and networks, and an invitation to political involvement, as well as identifying the common experience of external motivating factors that compel political action, and the transferable skillsets gained through social work education. Findings are particularly relevant to social work professional associations and schools of social work. Recommendations emphasize strategies and research that will help better understand the extent of social workers’ participation in Canadian electoral politics, and strategies to normalize and encourage greater levels of engagement among social workers.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.030 | 0.015 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 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".