Bibliographic record
Abstract
Being a universal social institution, marriage is generally viewed to represent a very important social achievement in Nepal because its incidence is influenced by multiple socio-economic factors to differing extents. Though there has been a slow shift in the marriage age, child marriage remains prevalent, especially in rural women. It is interlinked with health as well as education and economic dimensions. The current paper attempts to discuss some proximate socioeconomic and cultural factors that shape female age at marriage in Dhankuta Municipality, as a semi-urban area where age-old customs are blending with modern social norms. This study used a descriptive research design based on primary data from 185 evermarried women aged between 15 and 49 years collected through a structured questionnaire. Additional information was gathered from national census records and other secondary published and unpublished sources. The analysis has been carried out based on averages and percentages to trace the pattern and degree of association between some selected variables with the help of cross-tabulation with marital age. A mean age at marriage of 20.05 years has been observed to vary significantly by the education, occupation, and background of the family of women. The findings reveal that women who are literate and have attained higher schooling, and respondents from families where the main occupation is not agriculture, consistently marry later than their counterparts. Other determinants were age at menarche, type of marriage, and family structure where later biological maturity, nuclear household, and nonarranged union have a positive relationship with marital age. It reconfirms that the determinants of female age at marriage comprise an interplay between educational achievements, economic conditions, cultural, and biological factors. Immediate policy interventions include girls’ education, non-agricultural job opportunities as well as awareness campaigns are suggested.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".