Bibliographic record
Abstract
This presentation provides systematic evidence on an emergent debate about a Muslim woman's dress and her perceived credibility as a witness in a court context. The objective of this research is to understand how Muslim women's dress impacts their perceived credibility. The issue of testifying in western courts while wearing either a head veil (hijab) or a face veil (niqab) has been strongly contested on the grounds that physically seeing the witness's face helps observers judge her credibility (e.g., R. v. N.S., 2010). Canada's Supreme Court (N.S. v. Her Majesty the Queen, et al., 2012) ruled that judges will disallow the niqab "whenever the effect of wearing it would impede an evaluation of the witness's credibility." In 2010, an Australian judge ruled that a Muslim woman must remove her full veil while giving evidence before a jury. In 2007, the UK guidance on victims also indicated that the niqab may affect the quality of evidence given in the court room. All of those decisions and opinions run contrary to systematic research. Psychological studies suggest that nonverbal cues are not only poor indicators of veracity, but that they are the least useful indicators of deception (e.g., DePaulo et al., 2003, Vrij, 2008). This presentation discusses results of a research project that examined Muslim women's credibility. Using a quasi-experimental design, three groups of Muslim women lied or told the truth while testifying about a mock crime: 1) women without any coverings (safirat), 2) women wearing hijab (muhjabat) and 3) women wearing niqab (munaqabat). Videos of these women were then shown to audiences who assessed the credibilityof the witnesses. The research further explored the witness's general perceptions of being an eyewitness within the court system, and to what extent (if any) their dress impacted their perceptions. The presentation fits within the Social Sciences, Arts and Humanities thematic pillar of Qatar's National Research Strategy. It more particularly fits within two of the grand challenges: Managing Transition to a Diversified Knowledge- based Society: Build a knowledge-based society by emphasizing a robust research culture, and Holistic and Systematic Assessment of the Rapidly Changing Environment: Foster motivation, scholarship, and prosperity among Qatari nationals and expatriates along with cultural accommodations that are in-sync with modern practice.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".