Is the (traditional) Galilean science paradigm well suited to forensic science?
Bibliographic record
Abstract
Abstract For more than 10 years, forensic science has been at best, criticized for its lack of scientific foundations and at worst, presented as an oxymoron. An exclusive focus on standard operating procedures and quality management could cause forensic science to fall short of addressing the epistemological issue initiated by judges. This is particularly so in rapidly changing times, including digital transformation of society and decentralization of forensic services. As a consequence, the present understanding of forensic science by both scientists and its stakeholders is questioned. It is argued that that forensic science fundamental principles and, more broadly, forensic science philosophy are pivotal to the reliable application of science to address security and justice questions. This article is categorized under: Forensic Science in Action/Crime Scene Investigation > Epistemology and Method Jurisprudence and Regulatory Oversight > Communication across Science and Law Jurisprudence and Regulatory Oversight > Expert Evidence and Narrative
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 | 0.037 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.085 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".