Bibliographic record
Abstract
Novel scientific evidence challenges courts in terms of how they can evaluate reliability for the purposes of making admissibility decisions and assigning probative value to information that is adduced before them. An example of such problematic evidence is forensic gait analysis evidence which is in its infancy as a discipline of forensic science. This chapter reviews how objections to forensic gait analysis evidence have been handled in judicial decisions at first instance and on appeal in Canada, the United Kingdom and Australia. It identifies vulnerabilities in such evidence, especially when jurors are required to incorporate expert opinions (often from podiatrists) about the similarities in gait between that of the accused and a person seen on CCTV footage. The chapter expresses concern about the current scientific basis for such evidence in the absence of well developed databases in relation to gait characteristics, difficulties that characterise interpretation of CCTV footage, and the role that subjective issues can play in analyses by experts in gait interpretation. It notes a United Kingdom initiative in formulating a code of practice for forensic gait analysts but calls for caution in relation to reception and weight to be attached to such evidence until its scientific status becomes more developed.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".