Expressing the value of forensic science in policing
Bibliographic record
Abstract
Only a small part of forensic science activities scattered across criminal justice systems is the object of scientific scrutiny, and is taken into account when evaluating the added-value brought by this discipline. Even in its more restricted definition, forensic science faces many embarrassing questions about its capacity to provide valid and reliably interpreted information in court. The inflation of control mechanisms increases costs and reduces the scope or availability of forensic information. The viability of forensic science, viewed through this lens, is questioned. To address this challenge, it is imperative to validly express forensic science contributions that are otherwise diluted across earlier processes. These include abductive and inductive species of inferences used in crime investigation, crime analysis and criminal intelligence. The ‘scientificity’ of these processes may be questioned, but it is not contested that they largely determine the global outcome of justice systems. As a result, they cannot be ignored. To unlock the debate, it is proposed to turn the forensic science focus from means (instruments, techniques, methods) to ends (what is the problem, what are the objectives?). This perspective naturally leads to proactive models of policing. It also provides possible frameworks to express various uses of the information conveyed by traces for solving problems. Reframed forensic science contributions are more validly expressed and the current debate can ultimately be transcended.
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 | 0.110 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.015 | 0.132 |
| Scholarly communication | 0.041 | 0.039 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.021 | 0.015 |
| 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".