Jurors' Perceptions of an Elderly Eyewitness: Effects of Geriatric Diagnosis, Level of Care and Age
Bibliographic record
Abstract
Mock jurors' perceptions of older adult eyewitnesses was assessed by testing how age, geriatric diagnosis, and level of care influences decision making.Mock jurors (N=355) were asked to read a trial transcript that varied age of eyewitness: 45 years, vs. 65 years, vs. 85 years; Level of care: home vs. long-term care facility; and Geriatric disease: none, vs. early stage dementia.Mock jurors then rendered a verdict, provided ratings of the eyewitness, and completed a measure of stereotypes.Although no direct effect on verdict was found, verdict confidence was influenced in a statistically significant way by the presence of a geriatric diagnosis.Subscribing to negative stereotypes of older adults was found to be related to higher ratings of senility.The findings indicated that mock jurors are influenced by geriatric diagnosis, as it negatively impacts their confidence in their verdict.Limitations and future directions will be discussed.Defence: Was there any evidence that Mr. Turner had been the one to place the backpack in the dumpster?Witness: No, it was recovered but there were no witnesses; however, based on the location of arrest it did not rule him out as a suspect.Defence: Where did you present the lineup?Witness: At Mrs. Collins' home/Long-term care facility.Defence: Did Mrs. Collins hesitate or seem unsure
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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 | 0.030 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".