Eyewitness identification and surveillance of facial images: progress and problems
Bibliographic record
Abstract
Eyewitness testimony is older than the law.Even today, with sophisticated forensic science, eyewitness testimony forms the bedrock of many criminal cases.Whenever a witness gives testimony in court, jurors, judge(s) or magistrate(s) are faced with two basic questions: Is this witness giving an honest account?If so, can their account be relied upon as accurate?There are many reasons why a witness may deliberately give false testimony or identify a defendant they know to be innocent.The witness may be seeking revenge, have been intimidated into giving a false account, or be motivated to deflect blame away from the true culprit.Legal procedure is designed to expose a dishonest witness.In an adversarial system, for example in the UK, US, Canada, Australia and New Zealand, the defence have the right to test the testimony of prosecution witnesses through cross-examination.Equally the prosecution cross-examines witnesses for the defence.Cross-examination has been described as "the greatest legal engine ever invented for
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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; a candidate call from one teacher head, 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".