Parental Absence, Family Environment, and Adolescents’ School Performance: Evidence from the Three Southernmost Provinces of Thailand
Bibliographic record
Abstract
The impact of parental absence on children remains inconclusive and needs more contextualized research. This analysis examines the impact of parental absence on adolescents’ school performance and whether the impact can be explained by the family environment. The outcome is measured using adolescents’ assessment of their school performance compared with their classmates. The parent-adolescent living arrangement is classified as the adolescents living with both parents, with only the mother, and without the mother. We measure family environment using the family function (APGAR), parental/carer monitoring, and the parent-adolescent relationship. We used data from a household survey conducted in 2021 in Thailand’s three southernmost provinces where migration of young people to Malaysia is common. The sample includes 358 adolescents aged 13–17 years old and currently in school. Findings show significant negative though indirect impacts of maternal absence on the adolescents’ school performance. Also, that the significant impact of maternal absence is mediated by the family environment, particularly family function. Significant evidence of the effect of family environment on adolescents’ academic outcome, net of the parent-adolescent living arrangements particularly parent/carer monitoring, is highlighted.
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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 | 0.002 |
| Science and technology studies | 0.001 | 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".