PATTERNS IN CRIMINAL ACHIEVEMENT: WILSON AND ABRAHAMSE REVISITED
Bibliographic record
Abstract
Even though intense cultural pressures for monetary success and an institutional social structure dominated by the economy are viewed in anomie theory as stimulating criminal motivations and accounting for criminal behavior with an instrumental character, patterns in criminal earnings have not attracted much scholarly and empirical attention. Wilson and Abrahamse's (1992) analysis of Rand's second inmate survey concluded that most inmates interviewed during the survey had overestimated their monthly criminal earnings in an effort to rationalize their poor criminal performances. In this paper, we conduct, using Rand's first survey, a reanalysis of inmates' self‐reported monthly earnings. We conclude that meaningful patterns in criminal achievements easily emerge when allowed to do so. These patterns offer a telling story about differential criminal opportunities. Wilson and Abrahamse's emphasis on temporal inconsistency and response bias (boosting past benefits of crime) misrepresents the facts of that story and misjudges those persons agreeing to tell it. It is concluded that for a “criminal subculture” to have any persuasive or binding effect, its participants must be reasonably assured that their chances of making “crime pay” are not so remote as to become unattainable.
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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 | 0.012 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".