How do defendants choose their trial court? Evidence for a heuristic processing account
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
Abstract In jurisdictions with two or more tiers of criminal courts, some defendants can choose the type of trial court to be tried in. This may involve a trade-off between the probability of acquittal/conviction and the estimated severity of sentence if convicted. For instance, in England and Wales, the lower courts have a higher conviction rate but limited sentencing powers, whereas the higher courts have a higher acquittal rate but greater sentencing powers. We examined 255 offenders’ choice of trial court type using a hypothetical scenario where innocence and guilt was manipulated. Participants’ choices were better predicted by a lexicographic than utility maximization model. A greater proportion of “guilty” participants chose the lower court compared to their “innocent” counterparts, and estimated sentence length was more important to the former than latter group. The present findings provide further support for heuristic decision-making in the criminal justice domain, and have implications for legal policy-making.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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 it