Bibliographic record
Abstract
This paper is on the topic of gambling winnings and how silver (coin/money) can sometimes, if the correct set of circumstances exist, be turned into tax gold (tax payable to the Australian Tax Office). This is a topical issue due to some recent publicity in Australia about the so called Punters’ Club and their reported annual profit of $50 million gained from some $2 billion worth of bets placed annually and how the Australian Tax Office (ATO) is looking to tax the 19 identified members of this ‘club’ on their respective share of these winnings. These club members are of course choosing to ‘gamble’ against the ATO that they should not be taxed. The paper looks at the various approaches taken by Australian courts over the last century to the issue of whether gambling wins are assessable and highlights that of critical importance to the resolution of this issue is whether the gambling activities are carried on in the form of a business activity in a systematic and organised manner and whether the gambling activities involve a significant element of skill as opposed to mere random outcomes. The paper also considers the approaches to gambling cases taken in other similar tax law jurisdictions to Australia, such as the United Kingdom, Canada and New Zealand, in order to reveal common threads applicable to judgments across these different jurisdictions. Based on the principles of case law identified, the paper also considers the likely ‘chances’ of the Punters’ Club’s success in its arguments against the ATO.
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.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".