Men in His Category Have a 50% Likelihood, But Which Half Is He In? Comments on Berlin, Galbreath, Geary, and McGlone
Bibliographic record
Abstract
When scientific methods are applied to various questions, the body of knowl-edgegeneratedinevitablybecomesmoreelaborateandspecialized.Thisprocessofspecialization usually occurs hand in hand with rapid empirical progress. A goodexample is provided by the recent explosion in genetics. Not very long ago, myfreshman course in biology permitted me to follow the gist of the scientific work inmolecular genetics. Such is not the case now. Without very extensive preparation,I could not attempt to participate in this field, even as a commentator.Berlin, Galbreath, Geary, and McGlone (this issue) have provided what isessentially a layperson’s commentary on the recent research in the field of riskassessment. One assumes they did so because they believe most readers of SexualAbuse are nonspecialists (i.e., laypersons with respect to this field). Unfortunately,the commentary by Berlin et al. suffers from mistakes due to the authors’ unfa-miliarity with the specialized field that risk assessment research has become.While not so explosive as genetics, there have been recent advances in re-search on the assessment of risk among serious offenders and sexual aggressors.These recent advances were facilitated by statistical technologies that have pro-vided the means to characterize the accuracy of assessment, select and combinepredictor items, and deal with differential opportunity to reoffend. At the sametime, progress in research on risk assessment has been facilitated by statisticaltechniques to permit the combination of results from different studies to arrive atconclusive statements about the existence and size of hypothesized relationships.As well, research has attempted to ascertain the best way to measure outcomesfor sex offender recidivism. Among researchers, some consensus has emerged on
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 | 0.019 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.043 | 0.052 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".