Dangerous decisions: A theoretical framework for understanding how judges assess credibility in the courtroom
Bibliographic record
Abstract
Purpose. Numerous wrongful convictions have brought into question the ability of judges and juries to accurately evaluate the credibility of witnesses, including defendants. Dangerous decisions theory (DDT) offers a theoretical framework to build our understanding of the decision‐making process that can culminate in such injustices. Arguments. According to DDT, the reading of a defendant's face and emotional expressions play a major role in initiating a series of ‘dangerous’ decisions concerning his/her credibility. Specifically, potent judgments of trustworthiness occur rapidly upon seeing a defendant's face, subjectively experienced as intuition. Originally evolved to reduce the danger to the observer, the initial judgment – which may be unreliable – will be enduring and have a powerful influence on the interpretation and assimilation of incoming evidence concerning the defendant. Ensuing inferences will be irrational, but rationalized by the decision maker through his/her subjective schemas about trustworthiness and heuristics for identifying deceptive behaviour. Facilitated by a high level of motivation, a non‐critical, tunnel vision assimilation of potentially disconfirming or ambiguous target information can culminate in a mistaken evaluation of guilt or innocence. Conclusions. Empirically based education and responsible expert testimony could serve to reduce such biases and improve legal decision‐making.
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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 | 0.030 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.003 |
| Science and technology studies | 0.004 | 0.042 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".