Eyewitness Factors Influencing the Mock Juror Decision-making Process: Age, Familiarity, and Social Support
Bibliographic record
Abstract
Mock jurors (N = 281) read a trial transcript of an armed robbery at a convenience store.The transcripts varied by eyewitness age (10, vs. 15, vs. 20-years-old), degree of witness-perpetrator interaction (i.e.familiarity), and the degree of social support experienced by the witness during the crime (demonstrated through the presence/absence of a supportive figure such as a mother (i.e.high vs. low social support).The influence of these variables on jurors' perceptions of eyewitness' credibility, reliability, and accuracy as well as the decision of defendant's guilt, were investigated.The presence of social support influenced jurors' decisions regarding the defendant's guilt, where jurors in the low social support condition (i.e.alone) compared to the high social support condition (i.e. with mother) were nearly twice as likely to conclude that the defendant was not guilty.No significant group differences were found among the other factors; namely, eyewitness age and familiarity.Witness: No, nothing out of ordinary.Defence: Thank you Stacy, nothing further your honour.The Crown calls their third witness, Katie Richardson, and she takes the stand.Crown: Hello Katie, can you please tell everyone how old you are? Witness: I am 10 years old/15 years old/20 years old.Crown: Can you please tell the court where you were
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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 | 0.001 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".