Re: OAG Docket No. 121--Comments on Proposed Guidelines to Interpret and Implement the Sex Offender Registration and Notification Act (SORNA)
Bibliographic record
Abstract
Sex Offender Registration and Notification Act of 2006 (SORNA), the Association for the Treatment of Sexual Abusers (ATSA) welcomes this opportunity to provide feedback regarding the application of SORNA to youth adjudicated within the juvenile court system, and to certain adult offenders. The Association for the Treatment of Sexual Abusers (ATSA) is a multidisciplinary membership organization comprised up of 2,500 professionals in the various areas of sex offender management. Our membership includes community corrections officers, policy makers, researchers, mental health professionals, law enforcement agents, polygraph examiners, and victim advocates. We have been in existence for over twenty years and our membership includes the world’s leading researchers on sexual violence as well as many of the most experienced sex offender management professionals in the United States and Canada. Our members are responsible for the management of all types of sexual offenders, including youth, adults, and those who are developmentally disabled. ATSA recognizes that sexual assault is a serious social problem with profound effects
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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.034 | 0.009 |
| Insufficient payload (model declined to judge) | 0.472 | 0.522 |
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".