Mate Selection and Female Age at Marriage: A Micro Level Investigation in Tamil Nadu, India
Bibliographic record
Abstract
This paper aims to examine the process of mate selection in determining the age at marriage of 600 ever-married women drawn from rural (300) and urban (300) areas of Salem district, Tamil Nadu. Results based on the Multiple Classification Analysis show that both in rural and urban areas, time taken to initiate marriage process after menarche, caste background, age difference between bride and bridegroom and consultation of women for their marriage have played a greater role in determining their age at marriage in that order. Further, in urban areas, consanguinity has exhibited a highly significant effect on their age at marriage. While the role of payment of dowry has some effect on age at marriage of women both in rural and urban areas, the practice of horoscope matching has such an effect only in rural areas. Contrary to the expectation, first-born daughters enter into matrimony comparatively at higher ages than their later-bom counterparts only in rural areas. A weak support to residential propinquity theory of mate selection is also noticed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".