Dangerous decisions: A theoretical framework for understanding how judges assess credibility in the courtroom
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it