Unionizing Retail: Lessons From Young Women's Grassroots Organizing in the Greater Toronto Area in the 1990s
Bibliographic record
Abstract
Unionization in the retail sector is not well researched or understood and the work of everyday organizing in retail sites is rarely discussed. This is surprising since the most common occupation for both women and men in Canada is now retail salesperson/clerk.1 Furthermore, retail work continues to be characterized by low wages, few, if any, benefits and job insecurity. Despite these conditions and the rapid growth of retail work as an area of employment, this sector remains one of the most unorganized in the Canadian labour force. In 2009, 29.5 per cent of Canadian workers were represented by a union but only about twelve per cent of retail workers were union members.2 Within the retail sector, unionized workers are concentrated in food retail/grocery and warehouse work.3 As a contribution to the larger project of understanding the challenges and possibilities of organizing retail workers, I examine two retail unionization drives in the Greater Toronto area in the 1990s which were led by young women. During this period of neoliberal capitalism, material and ideological attacks on labour unions and working-class livelihoods and identities were widespread. However, unionization continued to appeal to many workers who
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.009 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".