Constitutionalizing Women’s Equality in India: Assessing the Sabarimala Decision
Bibliographic record
Abstract
In 2018, in Indian Young Lawyers Association (IYLA) v State of Kerala, popularly known as the Sabarimala case, the Indian Supreme Court struck down a rule that prevented girls and women in the 10-50 age group from entering the Sabarimala temple in Kerala. The Court, in a 4-1 decision, held that the temple rule violated women’s right to equality and right to worship and was not protected under the right to religious freedom. Sabarimala is a landmark decision. For the first time,the Court insisted that all discrimination must be tested against constitutional values and discrimination that perpetuates stereotypes and disadvantage will not withstand constitutional scrutiny. It emphasised the importance of substantive equality in contesting discrimination against women and challenging the structures of oppression that exclude women. Finally, the Court linked women’s equality rights with equal citizenship. Focusing on the Sabarimala decision, this paper evaluates the recognition of women’s rights in Indian constitutional jurisprudence and assesses its transformative potential. The paper aims to place women’s equality rights squarely within constitutional discourse; to further develop understandings of justice that will constitutionalize women’s equality rights and ensure their inclusion in constitutional doctrine and discourse and to create salient changes in women’s everyday lives.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.022 | 0.042 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.005 | 0.010 |
| 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".