Who to whom and why--Cultural differences and similarities in the function of smiles.
Bibliographic record
Abstract
Who to whom and why 2... this is how you sweep a yard; this is how you smile to someone you don't like too much; this is how you smile to someone you don't like at all; this is how you smile to someone you like completely; this is how you set a table for tea;... Jamaica Kincaid (1978, p.29) The ubiquitous smile People smile. People smile in public and in private, when they are happy and when they are distressed, during conflict and as a sign of intimacy. People smile often. Chapell (1997) counted public smiles in malls, stores, stadiums, restaurants, etc. for 15, 824 children, adolescents, young adults, middle aged adults and older adults and found that across all age groups 35.3 % of the men and 40.3 % of the women smiled. Yet, not everyone smiles equally. Younger people smile more than older people, individuals of European descent smile more than Asians and women smile more than men, – or at least that is how the common gender stereotype describes women. The present chapter presents an analysis of the function of smiles, of the role of smiles in interpersonal perception, and on individual differences, especially cultural differences in smiling.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".