The promises and perils of developing a national sex offender recidivism database in Australia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it