Understanding the Living Wage in a Rural Context: Strengthening Economic Multipliers in Revelstoke BC
Bibliographic record
Abstract
Regardless of the roots of poverty, the consequences are a concern for governments at all levels. The manifestations of poverty are intensely local, and civic action, in the form of campaigns to increase minimum wages and/or institute a living wage, have sprung up in cities and towns across the developed world. Little research to date explores the living wage movement in a Canadian rural context. In 2016 we conducted a community-based economic impact study to map the potential effects of a living wage proposed as a poverty reduction strategy for Revelstoke BC. The project partnered anthropologists and economists with local stakeholders to examine the community’s concerns about the initiative, and in particular its impact on local small businesses. Many of the benefits of a living wage campaign are extraordinarily difficult to measure, while the likely range of impacts on labour costs and consequences are easier to trace. The project resulted in an interactive tool that can provide a sector specific business-based analysis against which the wider social benefits of poverty reduction can be judged. Instead of providing a conventional “bottom line” analysis, the tool evolved together with the community debate, with stakeholders participating in ongoing dialogue that included some attempt to capture the potential positive multiplier effects of a living wage. Ironically, the business sectors most at risk from increased wage costs are those most likely to benefit from pro-social consumer behavior. Reflecting on the ways the tool has been used (and not used) in Revelstoke since its development, we discuss the potential for leveraging living wage initiatives to counter emerging consumer trends, such as the radical increase in online shopping, that can shrink the anticipated multiplier effects of interventions. We propose drawing on participatory methodologies that can simultaneously strengthen multipliers while making them more visible within the community.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".