Agenda setting at the municipal level: A comparison of strategies in two campaigns to increase wage standards in a mid-size Canadian city
Bibliographic record
Abstract
In terms of understanding the social, economic and health conditions that will lead to viable communities, wage matters form a central consideration. Related to such are the concepts of how wage discussions transfer from the public to the policy arena through agenda setting methodologies. To that end, research in agenda setting theory rarely focusses on local level investigations, but this project examined two community-based parallel public campaigns focused on raising wage standards. The effectiveness of the “Windsor-Essex Living Wage” campaign and the “Windsor-Essex Fight for $15 and Fairness” campaigns was measured through an analysis of the level of public, political and media/social media penetration each group achieved as well as how effective they have been in accomplishing their campaign goals. Such was done to determine the effectiveness of the strategies employed in each to assess what factors had been successful in bringing the issue of increasing wage standards onto the municipal policy agenda and why. Through employing a research methodology which combined the cataloguing of public data found from traditional and new media sources, such as local newspapers, social media feeds and blog postings, as well as conducting interviews with politicians, political staff, other policy elites and other individuals identified through snowball sampling techniques as being knowledgeable about one or both campaigns, an examination of data was able to be undertaken which compared the organizational structures and distinctions between the two campaigns. Through the investigations undertaken, this research aims to provide a better understanding of how social movements can be more effectively organized and what strategies are successful in raising issue salience at the municipal level.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".