Bibliographic record
Abstract
This questionnaire study investigated the understanding, subjective experience, and meta-emotion of anger and amae (a Japanese emotion of wanting to be loved) in Japan and Canada. The participants were Canadian (n = 50) and Japanese (n = 47) undergraduates. The results for anger indicated that the understanding, subjective experience, and meta-emotion of anger were similar between the two cultures, while there were some differences in the elicitor of anger. The results for amae indicated that the Canadians didn't have a superordinate concept that includes both good and bad aspects of amae (although they had an understanding of good and bad amae considered separately). Canadians did not make a clear distinction between feeling amae themselves and accepting other people's amae and, unlike the Japanese, their meta-emotion did not change between good amae and bad amae. The discussion deals with the cross-cultural similarities and differences of these two emotions, and their implications for personally experienced emotions.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".