What is a neighborhood? A concept consensus review of recent criminological literature
Bibliographic record
Abstract
Since the Chicago School, neighborhoods have been a staple in criminology research. However, some criminologists argue that there is no consensus on the definition of a neighborhood. This is important because if criminologists cannot agree on the theoretical concept of neighborhoods, they cannot synthesize neighborhood research across multiple studies. To test the “no consensus” assertion, we conducted a concept consensus review of all articles using the term “neighborhood” in the top 10 journals in criminology from 2010 to 2020. We found 310 articles where the term neighborhood was an important concept in the study. Of these articles, only 15 provided an explicit conceptual definition. An additional 6 articles provided ambiguous conceptual definitions. We probed the content of conceptual definitions and found they varied widely, often omitting essential elements. Finally, we examined the operational definitions used in the 310 studies and found 50 unique operationalizations of neighborhood. Therefore, we conclude that there is no consensus about the theoretical meaning of neighborhood in criminology. Criminologists should either reach a consensus about the meaning of neighborhood or abandon the concept and use newer alternatives. • Some criminologists claim there is no consensus on how the field defines the term “neighborhood”. • We undertook a concept consensus review of recent criminology articles to verify this claim. • Findings suggest there is no consensus on the theoretical meaning of neighborhood in criminology. • When authors provided conceptual definitions of neighborhood, the definitions varied widely. • There were also 50 unique operationalizations of neighborhood.
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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 | 0.043 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.026 | 0.019 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.017 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".