Bibliographic record
Abstract
Foodsters United, a workplace organizing campaign by Toronto food couriers, shows that, even in the gig economy, the classic organizing methods work. The Foodsters successfully challenged their misclassification as independent contractors, got over 40 per cent of a large workforce to sign union cards, and triggered a union vote that they won with 88.8 per cent support. These victories were tempered by a devastating setback: their employer, Foodora, exited from Canadian markets. Nevertheless, what Foodsters United achieved through workplace organizing sustained its transformation into Gig Workers United, which is organizing all delivery platform workers in Toronto. Although platform companies like Foodora promote the idea that the gig economy is unprecedented, its historical continuities are more important than its discontinuities. This is also true of the workplace organizing in the gig economy. Foodsters United achieved substantial victories, not because they invented new organizing methods but because they adapted the classic methods, in often ingenious ways, to their gig economy workplace. This article is based on interviews with the campaign organizers. It is organized thematically according to classic workplace organizing methods, particularly those developed in the industrial organizing tradition, including organizing conversations, mapping, charting, leader identification, issue identification, and the creation of democratic organizations.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.029 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".