Beyond the quantitative and qualitative divide: The salience of discourse in procedural justice policing research
Bibliographic record
Abstract
The dominant methods of studying police have involved quantitative analyses of surveys and systematic social observations of police behavior or qualitative methods such as ethnographies and interviews. The same trend applies to procedural justice research in policing. In prior works, the question of how police officers and citizens interact in situ is absent. We argue that procedural justice police research should move beyond the quantitative/qualitative distinction and consider other ways to collect and analyze data. We begin by providing a methodological critique of procedural justice research, and demonstrate the assumptions of discourse in extant works before we provide a blueprint for how to incorporate discourse analytic methods in the study of procedural justice and policing.
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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 | 0.144 | 0.201 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.013 | 0.098 |
| Scholarly communication | 0.033 | 0.048 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.008 | 0.010 |
| 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".