The New Neighborhood Watch: An Exploratory Study of the Nextdoor App and Crime Narratives
Bibliographic record
Abstract
Community members use the Nextdoor App to prevent crime and circulate information when suspicious activity or criminal misconduct is afoot. The Nextdoor App operates like other popular social media platforms, but unlike Facebook and Twitter, it connects citizens based on geography instead of areas of interest. One unique aspect of the app is posting events and perceptions of suspicious behavior and criminal acts. User posts can provide narratives on feelings, incidents, and perceptions of crime in designated neighborhoods. This exploratory study focuses on how community members in an urban Colorado area use the app as a high-tech Neighborhood Watch. A qualitative research approach with a thematic analysis is implemented to examine neighbors’ perceptions of crime events and community safety. The findings depict that citizens engaged in Nextdoor communications are frustrated with petty community incivilities, property crimes, and law enforcement actions. In addition, the results show that lessons in being a capable guardian, possible increases in fear levels, and surveillance activities are important aspects of understanding social media and crime.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".