Human Effectiveness and Risk Characterization of the Electromuscular Incapacitation Device - A Limited Analysis of the TASER. Part 2. Appendices
Bibliographic record
Abstract
Non-Lethal Weapons (NLWs) are becoming increasingly important assets in nontraditional military operations, such as peacekeeping missions or humanitarian aid operations, where the use of lethal force may not be a desired first response for force protection. NLWs are weapons that "are explicitly designed and primarily employed so as to incapacitate personnel or materiel, while minimizing fatalities, permanent injury to personnel, and undesired damage to property and the environment" (DoD, 1996). Various types of weapons are part of the Department of Defense (DoD) non-lethal weapons program, employing riot control agents, electromagnetic, mechanical, or acoustic technologies. DoD Directive 3000.3 calls for these weapons to "achieve an appropriate balance between the competing goals of having a low probability of causing death, permanent injury, and collateral materiel damage, and a high probability of having the desired anti-personnel or anti-materiel effects: (DoD, 1996). In an effort to achieve this balance the Joint Non-Lethal Weapons Human Effects Center of Excellence (JNLW HECOE) requested that Toxicology Excellence for Risk Assessment (TERA) organize a workshop of leading risk assessment experts, who were joined by Subject Matter Experts (SMEs) from the DoD and its contractors, to develop a framework for characterizing the risks from military use of NLWs. The results of risk characterization are to provide decision-makers with the probability of intended target response effects and unintended effects so that the risk could be weighed against the effectiveness and benefits of using NLWs. The TASER International Database (TI data) was provided by TASER International in July 2003. The TI data consists of 3,459 records submitted by individuals in the U.S. and Canada. The TI data includes information on a specific use of a TASER. The report includes information on the target individual, how and why the device was used, the number of shots fired, and the outcome. 7
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.066 | 0.015 |
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".