Apprehending Criminals: The Impact of Law on Offender-Based Research
Bibliographic record
Abstract
The past quarter century has witnessed the emergence of a rich methodological literature devoted to various ways of tapping into the offender’s perspective on crime. Whatever its virtues, that literature has remained almost wholly atheoretical. We recently introduced a preliminary theory of research grounded in the perspective of pure sociology. In this chapter we seek to extend that theory by examining how law and normative status affect offender-based research. We argue that as more law is applied to actors (i.e., as the normative status of persons or groups declines), the probability that those actors are recruited for offender-based research increases, the amount of remuneration provided to them for participation decreases, and the quality of data obtained from them decreases. We conclude by offering theoretically situated, practical advice about the ways in which criminologists might maximize data while minimizing costs associated with recruitment and remuneration.
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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 | 0.303 | 0.547 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.013 | 0.101 |
| Scholarly communication | 0.028 | 0.044 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 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".