Best Practices for Implementing a Living Wage Policy in Canada: Using Community-Campus Partnerships to Further the Community's Goal
Bibliographic record
Abstract
The study explores one longitudinal case of engaged scholarship, the collaborative Best Practices for Implementing a Living Wage Policy in Canada: Using community-campus partnerships to further the community's goals presents best practices for implementing a living wage policy, based on surveys and interviews of living wage advocates across Canada. This paper is a product of the ongoing partnership between Vibrant Communities Canada and Carleton University which is conducting a seven-year, SSHRC-funded study on how community-campus relationships can use joint resources to create practice and policy changes in the battle against poverty. For eight months, a group of Master of Social Work students researched the status of the working poor and the progress of living wage campaigns in North America, and analyzed data collected through surveys and interviews with individuals engaged in living wage campaigns. Recommendations for best practices to implement a living wage policy are discussed and include (a) developing a core group of individuals, (b) engaging champions to extend the buy-in of companies, (c) establishing a positive framework for the campaign, and (d) dedicating more resources to research and knowledge. This work is intended to facilitate discussion and create real impact on minimum wage regulations and business practices, resulting in increased social inclusion for individuals who identify as living in poverty.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.042 | 0.011 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.002 | 0.004 |
| 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".