Regulating work in the gig economy: What are the options?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Paid work associated with digital platform businesses (in taxi, delivery, maintenance and other functions) embodies features which complicate the application of traditional labour regulations and employment standards. This article reviews the extent of this type of work in Australia, and its main characteristics. It then considers the applicability of existing employment regulations to these ‘gig’ jobs, citing both Australian and international legislation and case law. There is considerable uncertainty regarding the scope of traditional regulations, minimum standards and remedies in the realm of irregular digitally mediated work. Regulators and policymakers should consider how to strengthen and expand the regulatory framework governing gig work. The article notes five major options in this regard: enforcement of existing laws; clarifying or expanding definitions of ‘employment’; creating a new category of ‘independent worker’; creating rights for ‘workers’, not employees; and reconsidering the concept of an ‘employer’. We review the pros and cons of these approaches and urge regulators to be creative and ambitious in better protecting the minimum standards and conditions of workers in these situations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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 it