Bibliographic record
Abstract
The frequently cited 2009 National Academy of Sciences Report entitled "Strengthening Forensic Science in the United States: A Path Forward" has become a focal point of forensic science practitioners' discussions and research since its publication. One of its recommendations is "Standardized Terminology and Reporting". Little has been published to date on this topic, although conversations and dialogs on the subject are ongoing. The upshot of this communication is to draw attention to the problem of one term in particular, perimortem, which may be only the proverbial "tip of the iceberg" in the lexicon-related concerns of forensic scientists. Even if it is an isolated issue, it is one that reflects the need for a consensus on term use and definitions by interdisciplinary practitioners who are currently using the term haphazardly, to the confusion of colleagues and potentially finders-of-fact in the courts.
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 | 0.086 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.017 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 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".