The Effect of First-Hand and Second-Hand Knowledge on Perceived Group Homogeneity and Certainty About Stereotype-Based Inferences
Bibliographic record
Abstract
Stereotypes are often used to make inferences about others, yet can lead to problematic consequences, which get exacerbated when people are more confident in these inferences. The current research examines whether biases in people's first-hand and second-hand information about groups make groups appear overly homogeneous, leading to more confident inferences about group members. Supporting this, across two studies, groups appeared more homogeneous when people lacked first-hand information from personal experience with a group, as well as when stereotypes were based on second-hand information from the media or other people. However, only second-hand information increased confidence about group members, as lacking first-hand information reduced confidence about what groups and group members were like. Biases in homogeneity also had greater impact for typical rather than atypical group members. Thus, people may be especially confident in stereotype-based inferences when stereotypes are based on second-hand information and when group members appear typical of their group.
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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 | 0.007 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".