CULTURE, POVERTY, AND RELIGION AT A CROSSROADS: CAUSES AND IMPLICATIONS OF CHILD MARRIAGE IN THE SLUMS OF KARACHI
Bibliographic record
Abstract
Child marriage continues to be prevalent in South Asia, including Pakistan, despite national laws and international agreements prohibiting it. This article aims to discover the causes of child marriage and its implications for child brides. We identify gaps in the implementation of laws, good practices, and program designs, and we propose necessary initiatives. Utilizing a cross-sectional design, a mix of quantitative and qualitative information was obtained from focus group discussions and from a survey of 131 early married females. We found that the cause of child marriage in Pakistan is not simply poverty: it is also deeply rooted in social customs, cultural norms, and traditional and religious beliefs. The psychological, economic, social, and physical consequences of child marriage can be excruciating for these girls, whose education may be curtailed and who are likelier than their unmarried peers to experience emotional, sexual, and physical violence. This study identifies parental decision-making, societal pressures, and entrenched gender roles as key drivers of child marriage. A lack of awareness about the legal marriage age and limited access to education further exacerbate the problem. Our recommendations include standardizing the legal marriage age at 18 years, implementing mass birth registration campaigns, and ensuring access to education for girls. Community-based awareness programs should challenge cultural norms and promote the benefits of delayed marriage. Strengthening laws and empowering local authorities to enforce them are also essential. Moreover, comprehensive poverty reduction programs, vocational training for women, and education reforms are needed if the root causes of child marriage and its often devastating consequences are to be successfully addressed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".