Bibliographic record
Abstract
I am pleased to report that the continued growth of the journal in terms of the number and quality of submissions has resulted in an agreement between the Association for the Treatment of Sexual Abusers and Sage Publications, the publisher, to increase the journal’s frequency from six to eight issues per year, beginning this year. I would also like to take this opportunity to recognize some changes to the Editorial Board: After 20 years of service as a member of the Editorial Board, D. Richard Laws has decided to step down from the Editorial Board. I would like to thank him for his many contributions to the journal. Also, after many years of contribution to the Journal, Tony Ward has decided to step down from the Editorial Board as his research program is shifting to more broadly at correctional psychology and criminology; I thank him as well for all he has done. After her impressive tenure as president of the Association for the Treatment of Sexual Abusers, it is a pleasure to welcome Elizabeth Letourneau back to the Editorial Board. Last, I would like to welcome the following new additions to the Editorial Board, and look forward to working with them: Reinhard Eher, Melissa Grady, Chantal Hermann, Elizabeth Jeglic, Wineke Smid, and Karen Terry. I would like to close by drawing your attention to two recent calls for submissions to special issues that appeared in the February and March 2016 issues. The first (deadline July 29, 2016) is on the theme of testing causal theories about sexual offending, guestedited by Kevin Nunes and Chantal Hermann (Nunes & Hermann, 2016). The call builds on the strong interest and participation in a pre-conference seminar held in Montreal, Quebec, on October 14, 2015 as part of the annual meeting of the Association for the Treatment of Sexual Abusers. The second (deadline August 1, 2016) is on the theme of sexual abuse in institutional settings, guest-edited by Karen Terry and Andrew Harris, reflecting an increasing concern about sexual abuse in youth-serving organizations, religious institutions, schools, the military, and other workplaces (Terry & Harris, 2016).
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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.555 | 0.511 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".