A model of evidence production and optimal standard of proof and penalty in criminal trials
Bibliographic record
Abstract
The defendant is either innocent or guilty, which she, not the court or prosecutor, knows. The court convicts the defendant whenever its posterior probability of her guilt – which depends on the evidence presented – is greater than the standard of proof . Evidence production by litigating parties is a costly stochastic process. Subsequently, the optimal choice of standard of proof and penalty is analysed. The optimal standard of proof is increasing in the cost of convicting an innocent defendant and decreasing in the cost of acquitting a guilty defendant. Higher penalties may increase probabilities of both false conviction and false acquittal. Un modèle de production de la preuve et la norme optimale de la preuve et de la punition dans les procès criminels. On développe un modèle de production de la preuve par les parties en litige dans un contexte criminel. L’accusé peut être de deux types – innocent ou coupable – et il sait de quel type il est. Mais ni le tribunal ni le procureur n’ont cette information. Le tribunal ne va condamner l’accusé que si la probabilité a posteriori de culpabilité de l’accusé est plus grande qu’une certaine valeur seuil – la norme de la preuve. Cette probabilité dépend des preuves présentées par les parties au tribunal. La production de la preuve est un processus stochastique coûteux. Ce modèle de production de la preuve est utilisé pour analyser le choix optimal de la preuve et de la punition. Comme on pouvait s’y attendre, on peut montrer que la norme optimale de la preuve s’accroît à proportion que s’accroît le coût de condamner un innocent et décroît à proportion que s’accroît le coût de l’acquittement d’un coupable. Ce qui est plus surprenant, on peut montrer que l’accroissement de la punition infligée à un accusé trouvé coupable peut accroître les probabilités à la fois de condamnation et d’acquittement non fondés.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 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".