Toward a slow criminology of sociotechnical orderings: A tale of many youth repellents
Bibliographic record
Abstract
Taking the material turn can contribute to renewing the discipline and sustaining the development of a slow criminology. Treating objects as mediators and acknowledging their ontological multiplicities protect us from our reflex to condemn rather than analyze them. Using the example of the ‘youth repellent’, we document three of its instantiations: a spatial fluidity device; a pain delivery mechanism; and an environmental pollution agent. This exploration forces us to expand the borders of the discipline to embrace others such as audiology and epidemiology. While these detours slow our analysis, they are the price we must pay for doing justice to the messiness of the human and non-human associations that constitute the fabric of our world.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.013 | 0.145 |
| Scholarly communication | 0.017 | 0.025 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.009 |
| 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".