Tort Law and the Gig Economy: A Socio-Legal Study of the Perspectives of Service Providers
Bibliographic record
Abstract
Over the last decade, information technologies have allowed individuals to connect with each other in a way that may not have been possible before. A consequence of this was the rise of a new phenomenon, that has ostensibly disrupted pre-existing notions of employment, often characterised under the umbrella concept of the so-called gig economy. Inevitably, like any enterprise, the gig economy places risk into society. Tort has capacity to distribute liability risks in the context of service provision through its core doctrines, such as vicarious liability, non-delegable duties and direct duties owed to third parties, and employer’s duties owed to employees. However, many of these doctrines rely on the notion of employment to distribute risk. The classification of most service providers in the gig economy as independent contractors could disrupt this long-standing concept, meaning that the liability risks may be borne entirely by the service providers or, in some cases, the victim. This thesis seeks to empirically examine the challenges that the gig economy poses to tort. It will examine the assumptions made by legal doctrine to determine employment and will empirically analyse the social reality of the gig economy. It is argued in this thesis that if the assumptions made by legal doctrine do not match the perspectives of the actors it seeks to represent, tort law is diminished in its capacity to distribute risk. This thesis will identify any discrepancies and challenges and will present suggested legal responses.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".