Juror Decision-Making in a Child Trafficking Case: The Impact of Defendant and Victim Gender, Defendant Age, and Defendant Status
Bibliographic record
Abstract
There has been an increase in human trafficking in Canada from 2018 to 2019, which suggests a rising trend in human trafficking, and as such, the number of court cases are likely increasing. Because of this, the current study sought to examine how a defendant and victim’s gender, and defendant social status and age impacted mock jurors’ decision-making in a child trafficking case. Participants ( N = 584) read a mock trial transcript depicting a child trafficking case. They were then asked to render a verdict, answer questions relating to perceptions of the victim and defendant and rate their level of agreements on statements concerning sex and human trafficking. Although there was no effect on dichotomous verdict, mock jurors attributed higher guilt ratings to the male trafficker. Moreover, participants reported more favorable perceptions of the victim when the trafficker was female, and the victim was male compared to female. Participants also reported more favorable perceptions of the victim when the trafficker was of high social status and younger compared to older. Additionally, when mock jurors were well-informed about trafficking victim blaming did not occur. The results of the current study provide some insight into juror perceptions of child sex trafficking cases.
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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 | 0.009 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".