Bibliographic record
Abstract
The world can be represented by two layers of information: how it appears on the outside (outward appearance) and what it is on the inside (inner state). To what extent an outward appearance is assumed to reflect the inner state is fundamental to social inference and judgments. Conceptualizing inference in terms of the relationship between the outward appearance and the inner state generates an integrative interpretation for a wide range of phenomena. We showed that Chinese were more likely than Euro-Canadians to make inference of inner state that deviated from outward appearance, whereas Euro-Canadians were more likely than Chinese to infer a convergence between outward appearance and inner state (Studies 1-5). We observed these cross-cultural patterns in various contexts involving people or physical structures. Individual differences in correspondence bias or response bias did not explain these patterns. The lay belief that outward appearance can be misleading mediated the cultural effects (Study 4). To probe the underlying process, two additional experiments showed that highlighting the misleading nature of appearance, but not highlighting the power of the situation, reduced Americans’ beliefs (Study 6) and inference (Study 7) that the outward appearance reflects the inner state. By focusing on the assumed relationship between the outward appearance and inner state, these findings provide a unique angle for understanding cross-cultural phenomena and have practical implications in daily life.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.068 | 0.085 |
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".