Distinguishing between homicide and suicide knots and ligatures: A comparative analysis of case and survey data
Bibliographic record
Abstract
Distinguishing between suicide and homicide can be challenging owing to ambiguous and confusing case details. In particular, there is a paucity of useful information comparing homicide and suicide knots and ligatures in the literature. Multiple knot and ligature characteristics have not been recognized previously because complete and accurate information has been lacking. Ninety external tying cases (mainly homicide) and 56 cases involving self-tying (mainly suicide) were reviewed to compare multiple knot and ligature characteristics. Additionally, 189 survey volunteers performed four standardized external tying tasks and two self-tying tasks, yielding comparison data from more than 1500 knots. Using all available data, it was determined that the differences between external and self-tying included types of knots, ligature configurations, ligature tensions, wrist gaps, wend lengths, knot access, external anchoring, and other indicators. These differences are presented in a proposed analysis checklist inspired by medical and psychiatric protocols. The checklist contains 14 potential characteristics for head and neck bindings, 14 potential characteristics for wrists and arms, nine ankle and leg characteristics, and four linkage details. The proposed checklist can be expanded and fine-tuned as more data are accumulated. It offers experienced knot analysts an additional tool to assist in drawing conclusions, which then can be related to other case evidence by investigators.
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 | 0.011 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".