The Challenges and Rewards of Carrying Out Qualitative Research on the Police in the African American Community
Bibliographic record
Abstract
In this paper, we discuss the challenges and rewards of carrying out qualitative research on the police in the African American1 community. Using data drawn from interviews with seventy-seven African American adults in Durham, NC, we found that community member hostility toward research(ers) and fear of both neighbors and the police lowered African Americans’ willingness to be interviewed about their perceptions of and experiences with U.S. police. These findings were observed primarily in public housing and middle-income communities. On a positive note, we found that greater awareness of policing issues increased African Americans’ willingness to participate in research about the police. This finding was more common among upper-middle-income African Americans. The implications of our findings for future research and improved policing in the African American community are discussed.
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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 | 0.411 | 0.385 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.036 | 0.035 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".