Bibliographic record
Abstract
This chapter was written for a workshop whose stated topic was criminal behavior . Crime and criminals have been with us always. The idea of criminal behavior has not. In an unproblematic sense of the words, you can engage in criminal behavior without committing a crime. The burglar assembles the tools of his trade and sets out, mask in hand – criminal behavior, we'd unreflectively say – but he falls into bad company on the way, drinks too much, and becomes too drowsy to burgle the mansion. But, of course, that is not what is meant when talk of crime is replaced by a discussion of criminal behavior. We are supposed to think of a tendency or disposition to behave in a certain way. Crimes, we are to imagine, are committed not just (tautologically) by people who behave in a criminal way but by those with a propensity for criminal behavior. In simple statistical modeling, we find the expression “criminal behavior” meaning no more than criminal acts – offences against the criminal law – of any type: embezzlement, burglary, assault, rape, murder, bank card fraud (e.g., Rowe, Osgood, and Nicewander 1990). In that literature it appears that “acts” could be substituted for “behavior” without change of intended meaning. (My burglar who fell asleep before committing an offence did not engage in criminal behavior.) But in sociological, psychological, and genetic work, the word “behavior” is treated more seriously. Most often violent criminal behavior is in view, and not, for example, bank card fraud.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".