The bias in judgement: when “naïve” knowledge challenges expert knowledge in criminal trials
Bibliographic record
Abstract
Purpose The purpose of this paper is to offer a discussion on the socio-cognitive biases involved during a criminal trial, in accordance with the literature in this field. Design/methodology/approach Whether it is the biases of representation, availability or anchoring (Fariña et al. , 2003), they have been widely studied in social psychology and constitute a relevant angle of analysis in the judicial context. Findings This paper outlines the issues related to the reality of the judicial decision, the psychological dilemmas that arise from it, as well as the normative pressures underlying the need to rationalize the decision. Finally, the status of psycho-legal expertise and the importance given to it is also discussed with regard to these issues. Practical implications This paper may help provide the diverse socio-judicial actors with some elements for questioning the psychological mechanisms that may intervene in the decision-making and therefore create a sense of conscientization necessary to optimize the quality of decision-making. Originality/value This paper may help provide the diverse socio-judicial actors with some elements for questioning the psychological mechanisms that may intervene in the decision-making and therefore create a sense of conscientization necessary to optimize the quality of 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 | 0.125 | 0.491 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".