Mapping The Role Of Uniform Civil Code To Achieve The Goal Of Gender Justice
Bibliographic record
Abstract
Achieving gender justice in India demands a unified legal framework that guarantees equality and protection for every individual, irrespective of their personal laws. The Uniform Civil Code (UCC), aims to replace religiously based personal laws with a common set applicable to all citizens, plays a crucial role in this endeavor. This paper investigates how the UCC could foster gender justice by addressing the inconsistencies and inequalities embedded in current religious personal laws. These laws often perpetuate gender-based discrimination in areas such as marriage, divorce, inheritance, and adoption. By analyzing the impact of the UCC on gender justice, this study examines how a standardized legal framework could unify these laws and ensure equal rights for women across diverse communities. The UCC has the potential to correct these disparities and apply gender equality consistently, thereby overcoming systemic biases. This study indicates that while the UCC offers significant potential for advancing gender justice, its success hinges on addressing socio-cultural challenges and engaging relevant stakeholders. A carefully crafted UCC could greatly enhance gender equality, though its effectiveness will rely on thoughtful legislative development and strong enforcement.
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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 | 0.020 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".