Bibliographic record
Abstract
In a number of common-law jurisdictions, gig workers (that is, workers who provide services through the use of web-based digital platforms) have recently sought to claim labour protections reserved for employees, such as the minimum wage, sick leave, and protection from unfair dismissal. These cases often involve the application of the multifactorial common-law test of employment to this new context, and the outcomes turn on the specifics of each case. In addition, classification as an employee has ramifications for a variety of tax matters. In this paper, the author considers whether the tax rules currently in place to capture non-standard employment arrangements have sufficient flexibility to capture gig workers. The focus of the analysis is Australian taxes (in particular, income tax, compulsory retirement savings contributions, and payroll tax), but reference is also made to similar issues under the laws of Canada. The author submits that, with respect to Australian income tax, gig work does not present a substantial risk to the tax base as a legal matter; however, a risk to the national revenue base comes from the compliance gap that is exposed when workers are no longer covered by employers' withholding mechanisms but are not picked up by tax administration regimes designed with larger businesses in mind. The author suggests that reliance on the registration of small businesses through the Australian business number, coupled with a new mandatory reporting regime for gig work platforms, would go a long way toward filling the transparency gap, and that doing so would both foster the voluntary compliance of gig workers and provide revenue authorities with data that could be used to detect non-compliance. A real risk exists that many gig workers will be outside the scope of the retirement contributions scheme and payroll tax and that the government, in consequence, will need to consider whether it is appropriate policy to change the law to include these on-demand workers.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".