Finding Facts Fairly in Roberts and Zuckerman's Criminal Evidence
Bibliographic record
Abstract
This book review focuses on the fact-finding aspect of Roberts and Zuckerman, Criminal Evidence, a student text examining the law of evidence in England and Wales through the lens of the criminal trial. Roberts and Zuckerman "take facts seriously," in the intellectual tradition of prominent evidence scholars such as John Henry Wigmore and William Twining. They set out in an accessible fashion the four major theories of probabilistic reasoning: the classical doctrine of chances; statistical or frequentist reasoning; Baconian probability theory; and Bayesian probability. Noting that forensic reasoning must almost invariably be inductive, they discuss, with useful examples, how probability calculations can be based on statistical data or on common sense generalizations, which may be influenced by the psycho-social characteristics of the fact finder. While the authors discuss possible biases in the fact-finding process, and are aware of the emerging human rights/constitutional context for their subject, their approach is more attentive to rationality than to how the law can contribute to non-discriminatory fact-finding for groups who experience, or feel, a relative lack of legal or social credibility. It is important for people who are distinctively vulnerable, for example to wrongful conviction linked to membership in racialized or otherwise stigmatized groups, that discriminatory fact-finding be taken very seriously. While a general legal method, incorporating human rights standards, for analysing inferences would be ideal in terms of enhancing the legitimacy of forensic fact-finding, it may be that the law, and academic exposition of it, can only develop in an piece-meal fashion. The book makes an impressive contribution to that development.
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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 | 0.015 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.013 | 0.023 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".