The Use and Misuse of Prediction
Bibliographic record
Abstract
(including rapists) are 'not a homogeneous group ' (Canada. Working Group, Sex Offender Treatment Review 1990), then the definition of the 'typical ' rapist and the development of a typology of rapists should be considered a fruitless enterprise. There are no physical or psychological characteristics which distinguish rapists, or types of rapists, from their fellow men. About the only thing rapists have in common, apart from their commission of the crime, is their shared social attitude towards women: this is summed up neatly by one of the subjects in Gebhard's classic study: Man, these dumb broads don't know what they want. They get you worked up and then they try to chicken out. You let 'em get away with stuff like that and the next thing you know they'll be walking all over you (Gebhard et al. 1965, p. 205). In parallel with this abandonment of taxonomic schemes, there has been despair about our apparent inability to predict which known rapist will attack again, at which time and in what circumstances. Not only have empirical studies on the prediction of violence generally shown that most predictions of dangerousness turn out to be false positives (Steadman & Cocozza 1974) but also such predictions with respect to 'sexual psychopaths ' lead to excessive periods of indeterminate incarceration in poorly resourced facilities with little hope of any 'treatment ' (Kittrie 1971). This is a gloomy picture. It is tempting to simply let sentencers, parole boards, clinicians and correctional officers get on with their work as best they can, using essentially a retributionist model dressed up with a little bit of rehabilitation. Yet there are cogent reasons
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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 | 0.108 | 0.275 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.016 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| 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".