The rise of biocriminology: Capturing observable bodily economies of ‘criminal man’
Bibliographic record
Abstract
Revisiting the contributions of numerous foundational biocriminological works, this article uses the concept ‘bodily economies’ to analyze the emergence and solidification of criminological pathologizations of the bios dependent on the capture and analysis of human corporeal matter. The scholars we discuss (Lombroso, Ellis, Goring, Hooton, and the Gluecks) each causally equate some part of the body with inbuilt criminality. Through an exegesis of their work, we illustrate how the boundaries of the social body are constituted in and through corporeal capturings and classifications of ‘criminal man’. Our analysis investigates the biocriminological method of locating sources of criminality inside the body, which still permeates the new ‘science of criminals’ used as a tool to define and protect the social body. We conclude by discussing the renewed biocriminological interest in preventing criminality through forecasting it in various scientific constructs and visualizations of the inner body.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.021 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".