Who to whom and why--Cultural differences and similarities in the function of smiles.
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
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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