Flexibility, choice, and labour law: The challenge of on-demand platforms
Bibliographic record
Abstract
Working through platforms is a recent but fast-growing phenomenon, with obvious implications for workers’ rights. Discussions have so far focused on the status of platform-based workers, but, recently, a growing consensus is emerging by courts around the world that workers for platforms such as Uber are in fact employees. As a result, legal disputes are likely to shift, to a large extent, from status questions to working-time questions. This might seem like a very specific issue, but, in fact, it has crucial implications for the entire model of platform work, and addressing this question requires us to rethink some of the fundamental pillars of labour law, notably whether more room should be opened for flexibility and individual choice within this system. We argue that one aspect of the platform model – ‘work on demand,’ which allows workers to log into the app whenever they wish to do so – poses a difficulty. Workers should be compensated for the time they are ‘on call’ and available to work. But platforms can be expected to respond by assigning workers to pre-set shifts to avoid paying for an unknown amount of working hours, thereby dismantling the ‘on-demand’ model. Such a change would be welcomed by many employees, who will gain more security, but others can be expected to object to losing the flexibility which they value. We consider possible solutions that could allow workers to choose the ‘on-demand’ model. While rejecting the possibility of allowing employees to waive on-call compensation rights, we consider several intermediate solutions that ensure partial payments for this time or exempt employees with another full-time job. The proposed solutions are based on the understanding that more choice is preferable in labour law as long as we can protect the interests of the affected employees and eliminate the externalities that some choices might generate for other workers.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".