Behind the Veil of Juror Decision Making
Bibliographic record
Abstract
Recent case law has considered whether a Muslim woman who wishes to appear in court should be allowed to testify wearing a veil that covers part of her face (e.g., Muhammad v. Paruk, 2008, in the United States, R. v. N.S., 2009, in Canada). The current research sought to test the influence of a victim’s wearing of a veil while testifying on juror decision making in a sexual assault trial. In addition, the study tested for effects of defendant race using Middle Eastern and Caucasian as the target races, given that there is a paucity of research comparing these races in the juror decision-making literature. Results demonstrated that contrary to hypotheses, jurors were more convinced of the defendant’s guilt when the victim was wearing a burqa or hijab to testify than when she testified wearing no veil. Defendant race did not have an effect on any of the dependent variables in this research; however, mock juror gender was found to be influential. Potential reasons for these findings and directions for future research are discussed.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".