Shifting partnership ideals with online technologies among unmarried women in India
Bibliographic record
Abstract
Abstract This study complements existing scholarship in family sociology and digital demography by investigating the role of digital technologies in shaping partnership ideals among unmarried women in India. We build on the premise that, by means of faster communication, effective information dissemination, and reciprocal exchange of norms and ideals, recurrent exposure to globalized cultural scripts through the Internet may shape family-related outcomes such as views and opinions regarding different aspects of family life. Leveraging new data from a primary survey of unmarried, partnered women living in cities across twenty states, we find that daily Internet use is positively and significantly associated with modern partnership ideals, measured as secularized views on the choice of a partner, the importance of marriage, partner preferences, and views about love marriage. Moreover, we show that accessing the Internet independently—vis-à-vis through a shared device—is what matters the most, and that results are stronger among high-educated individuals. We assess the selectivity of the sample by conducting subgroup analyses and replicating our findings on the National Family Health Survey (NFHS) 2019–2021. Lastly, we offer evidence that these findings can be deemed causal, complementing our results with an instrumental-variable approach leveraging digital geographical information. Our findings reveal that digital technologies may be gradually contributing to shifting views about marriage and family formation, even in a context such as India, which has traditionally exhibited strong resistance to modernization forces, at least in the realm of the family.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".