Maternal socialization of children's anger, sadness, and physical pain in two communities in Gujarat, India
Bibliographic record
Abstract
Despite the recognition of cultural influences in child socialization, little is known about socialization of emotion in children from different cultures. This study examined (a) Gujarati Indian mothers' reports concerning their beliefs, affective and behavioral responses to their children's displays of anger, sadness, and physical pain, and (b) their children's reported decisions to express felt emotion. Eighty mothers and their children (between 5 and 9 years) from two urban communities (suburban and old city) in Gujarat, India participated. Results indicated that Gujarati mothers considered their children's expressions of anger and sadness to be less acceptable than physical pain, and were more likely to convey to the child that the angry or sad expression was unacceptable than with physical pain. Mothers' beliefs about the acceptability of their children's displays were correlated with their reported behaviors in response to those displays, as well as with their children's decisions to express those feelings. Within-culture findings indicated that mothers in the old city considered their children's expressions to be less acceptable than mothers in the suburban community. The findings are discussed in the context of collectivist orientation, Hindu ideology, and social organization across the two communities that influence mothers' reported beliefs and behaviors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".