An assessment of the structural relationship between determinants and implications of caste‐based endogamy in Pakistan
Bibliographic record
Abstract
Abstract This study aims to examine the socio‐economic determinants and implications of caste‐based endogamy in Punjab, the largest province of Pakistan. It also measures the structural relationship between determinants and implications of endogamy. For this cross‐sectional study, 488 respondents were randomly selected from three Punjab province districts with inclusion criteria of being married within their caste. A path modelling and multigroup analysis were conducted using partial least squares structural equation modelling (PLS‐SEM). This study has found that social pressure, casteism, and fear of exogamy failure significantly influence mate selection in Punjab, which has socio‐economic implications. Social pressure and perceived easiness of endogamy less likely contribute towards economic implications. Similarly, casteism indicated a non‐significant influence on social implications. Overall, male and female respondents shared views on endogamy influencing determinants causing socio‐economic implications. This study concludes that caste‐based endogamy has a stronghold in the cultural patterns of Punjab, Pakistan, which restricts economic mobility, deepens the marginalisation of those from lower castes, and promotes casteism at community and institutional levels. This is one of the initial studies in Pakistani Punjab assessing the structural relationship between endogamy determinants and their social and economic implications.
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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 | 0.001 | 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) | 0.003 | 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".