Crime Prevention through Environmental Design: Discourses of Risk, Social Control, and a Neo-liberal Context
Bibliographic record
Abstract
This article explores the means by which crime-related risks are discursively framed by practitioners and supporters of Crime Prevention through Environmental Design (CPTED). It will argue that crime-related risks are framed in three interrelated ways: first, as forms of foreseeable danger; second, as depoliticized potentialities; and third, as potentialities that require complete responsibilization. It is argued that these discursive practices constitute the means by which practitioners legitimate their area of professional expertise while at the same time providing them with an opportunity to exercise pastoral control over their client(s). Furthermore, it is argued that these frames demonstrate how "risk" has become an important discourse through which neo-liberal governmentalities (Foucault 1991) with respect to crime control are actualized.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.097 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".