Surprised elaboration: When White men get longer sentences.
Bibliographic record
Abstract
. Across two public data sets, government officials wrote longer reports when negative events befell White people (stereotype-inconsistent) than when the same events befell Black or Hispanic people (stereotype-consistent). Officers authored longer missing child reports of White (vs. Black or Hispanic) children (Study 1a), and medical examiners wrote longer reports of unidentified White (vs. Black or Hispanic) bodies (Study 1b). In follow-up experiments, communicators found stereotype-inconsistent events more surprising and this prompted them to elaborate (Study 2). Surprised elaboration occurred for negative events (i.e., crimes, misdemeanors) and also positive ones (i.e., weddings; Study 3). We found that surprised elaboration has policy implications. Observers preferred to funnel government and media resources toward White victims, since their case reports were longer, even when longer reports were not more informative (Studies 4-6). Together, these studies introduce surprised elaboration, a new theoretical phenomenon with implications for public policy. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.003 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".