Situational and Ecological Predictors of Conducted Energy Weapon Application Severity
Bibliographic record
Abstract
Despite being touted as a “less lethal” use-of-force option, conducted energy weapons (CEWs) do pose some risk of injury to civilians, and thus warrant empirical examination. CEWs provide users with multiple use modes constituting various levels of severity; yet apart from the work of Somers and colleagues, almost no research exists investigating these levels of severity. Further, research findings on the impact of suspect resistance on CEW deployment are somewhat mixed. We contribute an innovative application of environmental criminology in a Canadian setting by exploring situational and ecological predictors of CEW application severity, with special attention being paid to reasons cited for CEW use and the impact of subject resistance level. Using all 393 Ontario Provincial Police CEW-related use-of-force reports over a two-year period, we find probe deployment to be the most common level of CEW application severity, irrespective of subject resistance level, and even when officers and subjects are in close proximity to one another. Application of CEW for the purpose of effecting an arrest is consistently the strongest predictor of CEW application severity without any mediating effect of subject resistance level or presence of a weapon. The impact of applying CEWs for the purpose of effecting arrests on CEW application severity is partially mediated by lighting visibility. Results are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".