The end of the (forensic science) world as we know it? The example of trace evidence
Bibliographic record
Abstract
The dominant conception of forensic science as a patchwork of disciplines primarily assisting the criminal justice system (i.e. forensics) is in crisis or at least shows a series of anomalies and serious limitations. In recent years, symptoms of the crisis have been discussed in a number of reports by various commentators, without a doubt epitomized by the 2009 report by the US National Academies of Sciences (NAS 2009 Strengthening forensic science in the United States: a path forward). Although needed, but viewed as the solution to these drawbacks, the almost generalized adoption of stricter business models in forensic science casework compounded with ever-increasing normative and compliance processes not only place additional pressures on a discipline that already appears in difficulty, but also induce more fragmentation of the different forensic science tasks, a tenet many times denounced by the same NAS report and other similar reviews. One may ask whether these issues are not simply the result of an unfit paradigm. If this is the case, the current problems faced by forensic science may indicate future significant changes for the discipline. To facilitate broader discussion this presentation focuses on trace evidence, an area that is seminal to forensic science both for epistemological and historical reasons. There is, however, little doubt that this area is currently under siege worldwide. Current and future challenges faced by trace evidence are discussed along with some possible answers. The current situation ultimately presents some significant opportunities to re-invent not only trace evidence but also forensic science. Ultimately, a distinctive, more robust and more reliable science may emerge through rethinking the forensics paradigm built on specialisms, revisiting fundamental forensic science principles and adapting them to the twenty-first century.
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 | 0.023 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.014 | 0.046 |
| Scholarly communication | 0.030 | 0.051 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.017 | 0.027 |
| Insufficient payload (model declined to judge) | 0.009 | 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".