Say NOPE to social disorganization criminology: the importance of creators in neighborhood social control
Bibliographic record
Abstract
Abstract Despite decades of research into social disorganization theory, criminologists have made little progress developing community programs that reduce crime. The lack of progress is due in part to faulty assumptions in the theory: that neighborhoods are important; that residents are the primary source of control; and that informal social controls are emergent. In this paper we propose an alternative: the neighborhoods out of places explanation (NOPE). NOPE starts with property parcels (i.e., proprietary places), rather than neighborhoods. It focuses on the power and legal authority of people and institutions that own property, rather than on residents. It posits that control is intentional and goal driven, rather than emergent. We refer to those who own and control as creators. This small group of elites shape city areas and residents must adapt to the environments that suppress or facilitate crime. We discuss how shifting our focus to creators provides important new implications for theory, research, and policy in criminology.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.034 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".