The promises and perils of developing a national sex offender recidivism database in Australia
Bibliographic record
Abstract
Much of what we know about sexual offenders and risk management is derived from empirical studies on sex offender populations in North America. In comparison to Canada and the United States, the evidence base in Australia on sexual offender risk management is under-developed. In this paper, we describe a current research project tasked with developing a national sex offender recidivism database to advance the evidence base in Australia. It is argued that a national database would advance knowledge and practice in the field of sex offender risk management in Australia in a multitude of ways. Yet there are many obstacles and difficulties in developing such a database. After putting forward a case for the need for such a database, we outline the issues we have encountered and the approaches we have adopted to develop this database. It is intended that this contemporary comment may not only alert readers to this emerging data resource in Australia but also function as a road map to guide future empirical research on offender population databases in Australia.
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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 | 0.087 | 0.167 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".