Bibliographic record
Abstract
This article takes as its point of departure the idea that the adoption of managerial principles to ensure the quality of DNA evidence is an accident of history which has changed the ways forensic biology is conducted and forensic biologists think. I begin by defining managerialism and tracking its entry into the contentious world of forensic biology, asking how it is that a focus on efficiency and precise process control is affecting these labs. My analysis unfolds in two parts. I first look at the external inspection routines that assure quality in forensic labs and the degree to which these routines represent ‘‘self” rather than ‘‘peer” assessment. I next look at the internal lab quality assurance (QA) routines that facilitate managerial control of technical and scientific workers, noting that QA is a trope flexible enough to govern both the numerically auditable and quasirobotic activities of technicians along with the less tangible more consensus-based human interactions of scientists. Illustrating that ‘‘science” is being pushed aside by management imperatives, I examine the consequences of this new emphasis for both the lab workers and the criminal justice system.
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 | 0.067 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.060 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| 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".