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Record W4283389515 · doi:10.52214/cjgl.v42i2.9046

Constitutionalizing Women’s Equality in India: Assessing the Sabarimala Decision

2022· article· en· W4283389515 on OpenAlexaff
Vrinda Narain

Bibliographic record

VenueColumbia Journal of Gender and Law · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicGender Politics and Representation
Canadian institutionsMcGill University
Fundersnot available
KeywordsSupreme courtJurisprudenceLawConstitutional rightSociologyCitizenshipOppressionPolitical scienceScrutinyEconomic JusticeConstitutional courtEqual Protection ClauseGender studiesConstitutionPolitics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.509
Threshold uncertainty score0.974

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.055
GPT teacher head0.365
Teacher spread0.310 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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