Technologies of Criminalization
Bibliographic record
Abstract
Technologies play a central role in decision-making processes within criminal legal systems, creating what we call technologies of criminalization. These tools are based on the idea of calculated truths about future riskiness, but they often reinforce structural biases that underlie the concept of criminality. Their development and use demonstrate efforts to define the abstract criminal: a notion that embodies the presumed natural realities and discoverable aspects of criminality believed to be objectively discoverable and statistically predictable. This perspective neglects the socially constructed nature of criminality and the impact of human biases in the design and implementation of these technologies. Three interlinked processes drive their adoption: quantification, prediction, and pathologization. By examining neuroscientific, genomic, and algorithmic technologies, we critically assess their social impacts and the risks of exacerbating social inequalities under the facade of technical neutrality. Finally, we emphasize the increasing involvement of private industries in criminalization processes.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.041 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".