Bibliographic record
Abstract
Over the last 20 years stock markets worldwide have changed dramatically and the Australian Securities Exchange (ASX) is no exception. First there was the wave of demutualizations when many stock markets, including the ASX, transformed themselves into public companies. Following these restructures many market operators hived off their surveillance and enforcement functions into organisations independent of the company operating the market. Existing market operators have started to move beyond their geographical boundaries and are becoming global conglomerates. New trading venues continue to appear. In Australia whilst there is only one dominant market operator, the ASX, this is likely to change soon. However despite these fundamental changes to the landscape, ultimately the job of protecting the integrity of the markets and the enforcement of serious market abuse offences still falls primarily on nationally based securities regulators, such as the Australian Securities and Investments Commission (ASIC). In fact ASIC’s grip on the markets has recently increased. From 1 August 2010 ASIC assumed responsibility of the supervision of brokers well as the surveillance of trading on the ASX, existing smaller markets as well as any new trading venues which may emerge. This article will consider the impact of these changes and consider the challenges that will face ASIC in maintaining the integrity of the markets as it attempts to bring them firmly under its control.
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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 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".