The stronger, the better? A natural experiment on the effects of pepper spray concentration levels
Bibliographic record
Abstract
Purpose Oleoresin capsicum (OC) spray has proven to be a relatively effective tool to subdue resistant or aggressive subjects without causing major and permanent injuries. Several products are available to law enforcement organizations and sprays with higher concentrations are sold as more effective, despite the lack of empirical evidence. This article aims to test the proposition that more concentrated OC sprays are more effective in a policing context. Design/methodology/approach To test this proposition, retrospective data on police interventions that had occurred (N = 1,019) were used. Concentrations were divided in three levels: level 1 sprays (containing up to 0.49% of major capsaicinoids), level 2 sprays (from 0.5% to 0.99%) and level 3 sprays (between 1 and 1.33%). Propensity scores were calculated to estimate the average effect of concentration level on effectiveness, while controlling for confounding factors. Findings Level 1 and level 2 sprays were found to be similar in terms of effectiveness, but level 3 sprays were found to be more likely to have an immediate effect but were also related to higher chances that decontamination was needed after use. Originality/value While several studies of the effectiveness of OC spray in general have been conducted in the past, this is one of the few to differentiate types of OC sprays according to their concentration level. As such, it aims to provide guidance to police organizations who must choose among a variety of products.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".