How does expertise really work ? Linking quantitative and qualitative analysis
Bibliographic record
Abstract
The aim of this poster presentation is to discuss the links between the quantitative and qualitative results of a study on the impact of psychological and psychiatric expertise on jurors’ decisions. In an accusatorial procedure (e.g in Quebec), trial decision making is a two pronged process of deciding the verdict and the sentence separetly (Beliveau & Pradel, 2007). This is not the case in inquisitorial procedure (e.g in France) where a judge and jurors decide the verdict and the sentence simultaneously. How do the jurors make use of expertise depending on the context of the judgment? In the present study, 134 French students from a variety of university departments were asked to read an indictment order transcript in which the presence of expertise was manipulated. In three conditions, mock jurors were exposed either to legals facts and expertise testimony (as in the inquisitorial system), or to judicial facts or expertise only (as in the accusatorial system). First, participants were asked a set of questions related to verdict, sentence, pronostic and the utility and credibility of the expertise. Additionally, there were open-ended questions about what motivated their decisions and what factors were more important. Results suggest that expertise impacts decisions on the voluntary nature of a crime, conviction attribution, degree of circumstances and risk of recidivism. On the other hand, expertise does not seem to influence jurors’ decisions on guilt and premeditation. However, results of qualitative analysis show that most jurors consider the content of the expertise as the most important factor motivating their verdict on guilt and premeditation. These results contribute to a reflexion about trial decision process by suggesting that although expertise did not directly impact certain decisions, jurors still use this information to justify their decisions, even though they do so unconsciously.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".