The Experience of Family in Japan and the United States: Working with the Constraints Inherent in Cross-Cultural Research
Bibliographic record
Abstract
Social scientists involved on cross-cultural research face a variety of challenges. This issue is discussed in the context of emic and etic approaches to research. A project illustrating some of these challenges is presented. A projective measure completed by each family as a group was used to capture the experience of family in Japanese and American families. Some culturebased hypotheses were confirmed, and an interesting serendipitous finding was explored in depth by a cross-cultural team. The unexpected finding was that pictures made by Japanese families, compared to those made by American families, were more likely to contain multiple images of the family. Further evaluation showed that the Japanese multiple images were most likely to reflect a textured, non-unitary experience, depicting a variety of contexts. The paper concludes by providing suggestions for enhancing the quality of cross-cultural research.
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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 | 0.040 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.024 | 0.017 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".