The Quest for Regulating Crime Scene Investigation in Forensic Science: Can We Learn About Discretion and Standardization From Other Fields?
Bibliographic record
Abstract
ABSTRACT Growing calls for better standards and quality assurance, particularly in crime scene investigations, have raised questions about the impact and suitability of these regulatory mechanisms on the forensic process. However, to date, debates on whether quality management strategies are “fit‐for‐purpose” have often overlooked how standards may influence the existing power dynamics inherent in forensic science and policing. Research in other sectors, such as public administration and policing, has nevertheless shown that frontline practitioners demonstrate a significant ability to resist standardization efforts. Police officers, teachers and physicians maintain considerable discretion in navigating and (re)interpretating standards to deal with the unique contingencies they face in the field and to respond to any perceived threat to their professional autonomy, competence, and identity. As crime scene examiners share many similarities with these other professional groups, this advanced review suggests that assessing the suitability of quality management strategies for standardizing crime scene investigation should better account for crime scene examiners' ability to negotiate, adapt, or even resist these strategies based on pre‐existing sociocultural practices. This calls for further empirical research into the actual effects—both positive and negative—that these standards have on perceptions, practices, and outcomes in forensic science.
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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 | 0.079 | 0.134 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.034 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".