Forensic science 2020 – the end of the crossroads?
Bibliographic record
Abstract
Forensic science has been at the crossroads for over a decade. While this situation is a fertile ground for discussion, security problem solving and the sound administration of justice cannot be put on hold until solutions pleasing everyone emerge. In all practical reality, forensic science will continue to be applied because it is simply the most reliable way to reconstruct the past through the exploitation of relics of criminal activities and by logical treatment of the collected information. In this paper, it is argued that instead of exclusively focusing on error management and processes, we should also question the very ontological nature of forensic science. Not only should the dominant conception of forensic sciences as a patchwork of disciplines assisting the criminal justice system be challenged, but forensic science’s own fundamental principles should also be better enunciated and promoted so they can be more broadly accepted and understood. Such changes invite operations, education and research to become more collective and interdisciplinary. This is necessary to fully exploit the investigative, epidemiological, court and social functions of forensic science. We ought to ask the question: will forensic science reach the end of the crossroads soon?
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 | 0.053 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.015 | 0.031 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.029 | 0.019 |
| Insufficient payload (model declined to judge) | 0.063 | 0.032 |
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".