Workers organizing in the platform economy: Local forms and global trends of collective action
Bibliographic record
Abstract
Abstract Distinctive features of the on‐demand work platforms made it theoretically improbable for workers to organize and for collective forms of protest to emerge. Their business model and work arrangements spatially isolate and socially individualize workers, subjectivizing them as competing micro‐enterprises rather than co‐workers. However, faced with the flood of the platforms on a global scale, collective actions of platform workers surged like a backwash, especially in the ride‐hailing and food delivery sectors, during the last decade. Observers witnessed a great variety in the combination of actors involved and repertoire of actions mobilized worldwide. Despite this diversity, some common global trends can be sketched out. Through a literature review focused on Europe, Latin America, North America and Asia, this article shows that workers struggle globally to build a collective actor, through an original combination of new and old forms of protest. They ought to compensate for their weak marketplace bargaining power by leveraging their discursive, associational, coalitional and workplace bargaining powers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".