Pigs vs people: the use of pigs as analogues for humans in forensic entomology and taphonomy research
Bibliographic record
Abstract
Most studies of decomposition in forensic entomology and taphonomy have used non-human cadavers. Following the recommendation of using domestic pig cadavers as analogues for humans in forensic entomology in the 1980s, pigs became the most frequently used model cadavers in forensic sciences. They have shaped our understanding of how large vertebrate cadavers decompose in, for example, various environments, seasons and after various ante- or postmortem cadaver modifications. They have also been used to demonstrate the feasibility of several new or well-established forensic techniques. The advent of outdoor human taphonomy facilities enabled experimental comparisons of decomposition between pig and human cadavers. Recent comparisons challenged the pig-as-analogue claim in entomology and taphonomy research. In this review, we discuss in a broad methodological context the advantages and disadvantages of pig and human cadavers for forensic research and rebut the critique of pigs as analogues for humans. We conclude that experiments using human cadaver analogues (i.e. pig carcasses) are easier to replicate and more practical for controlling confounding factors than studies based solely on humans and, therefore, are likely to remain our primary epistemic source of forensic knowledge for the immediate future. We supplement these considerations with new guidelines for model cadaver choice in forensic science research.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".