Confirmation bias: A barrier to community policing
Bibliographic record
Abstract
This is a very challenging time for police–community relations, one characterized by a mutual lack of trust between police and citizens. But trust is an important tenet of effective community policing. Trust between police and communities can result in better problem solving, fewer legal violations by citizens, less frequent use of force by the police, less resistance by citizens during arrests, greater willingness to share information, less inclination to riot, and greater willingness of community members and police to cooperate. One key obstacle to fostering trust between the community and police is confirmation bias—the tendency for people to take in information and process it in a way that confirms their current preconceptions, attitudes, and beliefs. Recognizing and addressing confirmation bias, therefore, plays a critical role in fostering more productive engagement. If we are to improve police–community relations and co-create a way forward, learning to approach debates with open minds, an awareness of the lens of our own perspectives, commitment to considering the opposite, and the goal of listening with curiosity are essential.
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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 | 0.045 | 0.175 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".