Do they all look alike? An exploration of decision-making strategies in cross-race facial identifications.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Meme si des centaines d'etudes ont demontre que le temoignage de temoin oculaire est sujet a l'erreur, la preuve d'un temoin oculaire est souvent la plus forte ou la seule qui est retenue par les jurys lorsqu'ils rendent un verdict. Une cause potentielle d'erreur se produit lorsque les temoins oculaires et le suspect sont de race differente. Les conclusions concernant l'effet transracial sont generalement uniformes, mais les causes de l'effet ne sont pas bien comprises. Cette recherche examine les strategies de prise de decisions qui peuvent differencier l'identification des suspects dans des situations transraciales par opposition a des suspects de meme race. Les donnees ont ete recueillies aupres de 161 sujets caucasiens engages soit dans une tâche de reconnaissance faciale transraciale ou de meme race, semblable a celle utilisee dans les enquetes criminelles. Bien que peu de differences n'aient ete trouvees entre les strategies de decision concernant les sujets de meme race et transracial, un certain nombre d'autres effets ont ete trouves, notamment l'incidence de la race sur la clarte de la memoire et la confiance avant et apres la decision. Nous decrivons la signification de ces donnees et proposons des axes de recherche future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.007 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it