“He was brainwashed!” Criminal complicity and sentencing in France: interpreting a “crime committed under influence”
Bibliographic record
Abstract
Purpose This paper aims to examine whether being shown a testimony alleging that the perpetrator of a crime was influenced by an accomplice has an impact on the severity of the sentence given to this accomplice. Design/methodology/approach A total of 119 participants read the summary of a case of armed robbery. Two experimental conditions were adopted: the presence of a testimony suggesting the accomplice’s influence on the perpetrator in committing the crime (versus no testimony). The participants were then asked what sentence they would give the accomplice and what sentence they would have given the perpetrator of the crime, who had in fact already been sentenced. The participants rated items relating to the explanation for the crime (perception that the perpetrator had been manipulated by the presumed accomplice) and to the presumed accomplice’s intent to commit the crime. Findings The participants showed themselves to be harsher towards the presumed accomplice when they were shown the testimony about his influence, which reduced the disparity with the sentence they would have given to the perpetrator of the crime. Analyses of mediation show that the participants shown the testimony (as opposed to those who were not) were more likely to say that the presumed accomplice manipulated the perpetrator of the crime, leading them to be more likely to attribute to the accomplice the intent to commit the crime and to be harsher towards him. Originality/value The results of this research are discussed with a focus on naïve interpretations of influence in the very specific context of legal adjudication.
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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 | 0.002 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".