Bibliographic record
Abstract
Geo-politically Australasia includes the countries of Australia and New Zealand. These countries share a similar colonial history, dominant culture, and political systems. They also have much in common with regards policing. 1 Australasia has a combined land mass of almost eight million square kilometres and a population of 28.6 million ( Statistics New Zealand, 2015 ; Australian Bureau of Statistics, 2015 ). This is a land mass 32 times the size of the UK, and comparable with the USA and Canada, although it encompasses a population only three and a half times the size of London or New York City. Australasia is served by nine police jurisdictions: The Australian Federal Police (AFP), The Northern Territory Police Force, The Queensland Police Service, New South Wales Police Force, Victoria Police, Tasmania Police, South Australia Police, Western Australia Police, and New Zealand Police. 2 These police organizations employ approximately 88,000 members, 65,000 of whom are sworn, with an average of 20% in formal leadership (i.e. sergeant and above) roles, with less than 5% at the rank of inspector or above. Just over 100 officers are at the senior executive level. In addition to these police organizations, there are a number of other state-based and Commonwealth law enforcement, regulatory, and investigative agencies (including the Australian Crime Commission, Australian Border Force, the Australian Transaction Reports and Analysis Centre, the Organized and Financial Crime Agency New Zealand, and New Zealand Customs). As well as a broader public safety base that includes fire and emergency services, each of whom have investigative capacities and contribute to public safety. There is, then, a considerable tapestry of state-run organizations involved in aspects of policing in Australasia.
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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 | 0.022 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.020 | 0.025 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.041 | 0.074 |
| Insufficient payload (model declined to judge) | 0.011 | 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".