Methodology matters: comparing sample types and data collection methods in a juror decision-making study on the influence of defendant race
Bibliographic record
Abstract
Researchers have expressed concerns that using online and/or student samples in juror decision-making studies significantly diminishes the trustworthiness of results. The purpose of this study was to test whether these samples might yield different demographics, attentiveness to a trial stimulus, and verdict decisions. Participants read a fabricated robbery trial transcript – in which we manipulated the defendant’s race (White, Black, Aboriginal Canadian) – then made verdict decisions and completed manipulation checks. We tested four Canadian samples: non-student community members online, non-student community members in-lab, students online, and students in-lab. Addressing one of the common criticisms of online samples, those who participated online were no more likely to fail manipulation checks than those who completed the study in-lab. We also found an interaction among data collection method, defendant race, and verdict – participants who completed the study online were more lenient towards White defendants, suggesting that the presence of a research assistant (and/or other participants) in the room while participants completed the study affected the expression of racial bias. Our findings allay some common concerns about online and student samples, but also show some limitations, including clear demographic differences.
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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 | 0.436 | 0.614 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".