Cinderella Story? The Social Production Of A Forensic “Science”
Bibliographic record
Abstract
The last decade has witnessed unprecedented criticism of the forensic sciences from academic commentators and authoritative scientific and technical organizations. Simultaneously, podiatrists have begun to promote themselves as forensic scientists, capable of assisting investigators and courts in their endeavors to identify offenders. This article traces the emergence of forensic podiatry, particularly forensic gait analysis. Forensic gait analysis is a practice that involves comparing persons of interest in crime-related images (such as CCTV and surveillance recordings) with reference images of suspects, where the primary focus is on movement and posture. It tends to be applied when other techniques, such as the comparison of facial and body features, are constrained because of disguises (e.g., the use of balaclavas) or the low quality of the images. This article endeavors to explain how forensic podiatry came into being, shed light on forensic field formation, make an assessment of the knowledge base underpinning forensic gait analysis, and reflect on what the legal recognition of forensic gait analysis reveals about the ability of common law courts to regulate expertise.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".