Strike Law and Workers’ Power Resources in Global Supply Chains and Platform Giants
Bibliographic record
Abstract
The right to strike is a key feature of freedom of association and effective collective bargaining. We consider how the legal regulation of strikes and boycotts affects the power resources available to workers and unions to improve working conditions and workers’ voice in firms, such as global supply chains and platform giants, that utilize network-of-contracts business models. We begin by bringing the literatures on power resources theory and supply chain and platform capitalism into conversation. Treating law as a form of institutional power influencing workers’ ability to exercise other power resources in network-of-contracts business models, we then examine how the laws regulating strikes influence workers’ ability to mobilize their other power resources to affect the terms and conditions of work. We investigate the Make Amazon Pay campaign and related strikes to gauge how the legal regulation of strikes affects workers’ power to disrupt supply and production under network-of-contracts business models. We conclude by highlighting the need to revise the law of strikes to fit the power relations under supply and platform capitalism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".