Influence of victim characteristics and crime type on eyewitness recall of perceived stereotypicality
Bibliographic record
Abstract
Previous research in Canada and the United States has shown that activated racial stereotypes about a given crime type can influence an eyewitness’ memory of perceived stereotypicality for the perpetrator of that given crime. Specifically, it has been shown that participants who are exposed to a surveillance video of a highly stereotypical Black crime (i.e., drive-by shooting) falsely recall the perpetrator from the video to be higher on perceived Black stereotypicality than those who are exposed to a surveillance video of a highly stereotypical White crime (i.e., serial killing). Victim characteristics such as the race of the victim (i.e., Black adult males vs. White adult males), in conjunction with crime type, were examined in the current research to determine their influence on the accuracy of eyewitness recall. The perceived deservingness of these victims was also examined. The results replicated and confirmed previous research that has shown how crime type (i.e., drive-by shooting vs. serial killing) can influence an individual’s eyewitness recall of perceived stereotypicality (i.e., M = 54.03 vs. M = 50.18, respectively). In the present research, however, the race of the victim did not exacerbate the effect of the crime type findings. The race of the victim did matter in terms of perceived deservingness, however, with Black adult males being viewed as more deserving of the purported crime than White adult males (i.e., M = 6.90 and M = 4.53, respectively). In the United States alone, eyewitness identification errors account for approximately 75% of all wrongful convictions. Of these wrongful convictions, 70% involve the wrongful conviction of individuals from minority groups. The findings of the present research will not only help to address the issues related to eyewitness (mis)identifications, but will also contribute to educating the public on how these errors may disproportionately impact certain minority groups, and the need for positive change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".