Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it