Criminality, Interpersonal Proximity and the Stop-Snitching Code: An Examination of Offender and Non-Offender Perceptions
Bibliographic record
Abstract
A number of studies have examined the relationship between the ‘code of the street’ and the concept of snitching (that is, informing the police). With some notable exceptions, these studies have generally focused on the pervasiveness of a ‘stop-snitching code’ or ‘code of silence’ among street offenders. In this study we seek to broaden understanding of the stop-snitching code by exploring perceptions of active, former, and non-offenders living in areas considered by residents to embody the street code. We find that informal cultural norms do in fact dissuade both offender and non-offenders from cooperating with police, but also that personal experience with police, proximity to offences and offenders, and types of crimes in question play major roles in the contextual framing of whether or not people choose to cooperate with the police.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".