The role of ethnicity and socioeconomic status in Southeast Asian mothers’ parenting sensitivity
Bibliographic record
Abstract
Past research indicates that socioeconomic status (SES) accounts for differences in sensitivity across ethnic groups. However, comparatively little work has been conducted in Asia, with none examining whether ethnicity moderates the relation between SES and sensitivity. We assessed parenting behavior in 293 Singaporean citizen mothers of 6-month olds (153 Chinese, 108 Malay, 32 Indian) via the Maternal Behavioral Q-Sort for video interactions. When entered into the same model, SES (F(1,288) = 17.777, p < .001), but not ethnicity, predicted maternal sensitivity (F(2,288) = .542, p = .582). However, this positive relation between SES and sensitivity was marginally moderated by ethnicity. SES significantly positively predicted sensitivity in Chinese, but not Malay dyads. Within Indian dyads, SES marginally positively predicted sensitivity only when permanent residents were included in analyses. We discuss the importance of culture on perceived SES-associated stress. However, because few university-educated Malays participated, we also consider whether university education, specifically, positively influences sensitivity.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".