Judging, Fast and Slow: Using Decision-Making Theory to Explore Judicial Fact Determination
Bibliographic record
Abstract
Empirical research with judges and jurors has provided research into the process by which legal decision-makers come to a view about the facts of the case. However, much remains uncertain, including questions about how judges' reasoning processes might differ from jurors' when thinking through the facts of a case, and how well the insights of decision-making research translate into the noisy context of real criminal trials. This article offers a preliminary exploration of connections between Pennington and Hastie's story model of decision-making, heuristics and biases research, and areas of fact determination that have presented persistent difficulties to criminal courts, including sexual assault, child homicide and the assessment of expert testimony. I discuss some of the key insights that cognitive psychology can offer to those who are interested in understanding how decision-makers think about the facts of a case, and where decision-makers may be prone to error.
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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 | 0.003 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".