The platform discount: Addressing unpaid work as a structural feature of labour platforms
Bibliographic record
Abstract
Digital labour platforms are able to structure work to limit paid working time, extract fees from workers to access labour, and shift costs associated with occupational safety and health (OSH) compliance onto platform workers. We call this unpaid work the ‘platform discount’. Unpaid labour is embedded within platforms’ competitive strategies as platforms operate with labour oversupply while clients use multiple platforms to search for the cheapest option (multi-homing effect). The authors study pathways through law that would limit the incidence of unpaid work by revisiting three areas of the legal framework: working time, safety and health, and access to work/labour intermediation. The authors argue that reclassification, suggested, among others, by the draft Platform Work Directive, can reduce the platform discount for the misclassified workers, but will leave solo self-employed unprotected. The authors explore two possible strategies to reduce the platform discount for the solo self-employed working on labour platforms: 1) a broader understanding of the concept of working conditions on digital labour platforms covering both standard employees and solo self-employed; 2) proceeding area by area, with the extension of occupational safety and health to the solo self-employed on digital labour platforms being the most feasible and promising from a regulatory standpoint.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".