Validating the LLIM Testing Model, Documenting Wounds/Injuries
Bibliographic record
Abstract
The cost of doing business in law enforcement is an expensive undertaking when one considers staffing, vehicles, uniforms, equipment, and training. What can cripple a police department, or even a city, are payouts from useof-force complaints and workers’ compensation claims from injured officers. Police chiefs from all over the world have struggled to manage the potential The “Sword-Man”: A Lesson in Less Lethal Options 4 Addressing the Threat 4 Less Lethal Deployment on Sword-Man 5 Training versus Reality 6 Adapting Tactics 6 Final Outcome and Lessons Learned 7 WTO: “The Battle in Seattle”—A Less Lethal Success 7 Event Review 7 Pre-event Police Planning 8 Line Officer Training 9 Tactical CART Preplanning 9 Pre-event Protester Planning 10 Event Realities 10 Priorities: Life Safety, Incident Stabilization, Property Protection 13 CART Ran Out of Ordnance 14 Political Fallout 15 Lessons Learned 16 Seattle Mardi Gras Riots 2001: Celebration Took a Turn 17 Pre-Event Decisions 17 Lessons Learned after Mardi Gras 2001 18 Canadians Love Hockey: Stanley Cup Riots, Vancouver, BC 20 De-escalation and Less Lethal Options 24 References 25 risk and minimize negative outcomes of police encounters. Furthermore, the political ramifications and shifting public opinion from high-profile incidents such as in-custody deaths or videotaped use of force (Rodney King beating) can affect police morale and even the safety of a jurisdiction (civil disturbances that escalate into riots). When force is used, people can be subjected to physical and/or emotional injury, which is usually associated with a monetary payout. Incorporating additional de-escalation tactics and training into police policy is a popular risk-management approach to mitigate these bad outcomes and reduce liability related to the use of force by the police. In reality, police officers have always applied de-escalation tactics, although it is commonly believed that de-escalation only applies to verbal tactics in calming a confrontational situation. Statistics have consistently demonstrated that in the vast majority of incidents, the responding police officers are able to control a situation with a number of tactics without resorting to the use of physical force. Studies done in the late 1990s by the International Association of Chiefs of Police (IACP) and the Bureau of Justice Statistics (BJS) show that only 1.4%–1.9% of the 40 million people who were contacted by the police had reported force or the threat of force at least once during the contact [1] (Figure 1.1). Other studies throughout the last decade have mirrored this low figure. The BJS report also unfortunately states that media depictions continue to create the perception that use or threat of force by the police is greater than it actually is. However, the outcry remains for officers to attempt de-escalation techniques to reduce the perceived increase of force used on the public.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".