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Record W3114997087 · doi:10.24908/iqurcp.9948

Social Reform with a Nationalist Agenda: The Sarda Act of 1929

2018· article· en· W3114997087 on OpenAlexvenueno aff
Hayley McNorton

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicDemographic Trends and Gender Preferences
Canadian institutionsnot available
Fundersnot available
KeywordsHinduismNationalismContext (archaeology)LegislatureColonialismLegislative historyCivilizationGender studiesSociologyPolitical scienceLawGeographyReligious studiesPoliticsPhilosophyArchaeology

Abstract

fetched live from OpenAlex

On September 20th 1929, the Indian Central Legislative Assembly passed the Sarda Act. The Sarda Act was the result of ongoing discussions in India in the early twentieth century that revolved around the age of consent and the age of marriage. Har Bilas Sarda, a member of the Assembly since 1924, introduced the bill in 1927 as the Child Marriage Restraint Act. In history, this bill is celebrated for improving the living conditions for women in Colonial India by addressing the potentially negative physical and social effects that having sex and giving birth could have on young girls. However, what is not discussed in history is the motivations that Har Bilas Sarda had in introducing the bill. Using primary sources, I would like to argue that the Sarda Act was part of a larger nationalist agenda espoused by a Hindu nationalist group called the Arya Samaj which aimed to restore the legacy of the ancient Hindu civilization. I will conclude by linking the themes in Sarda’s writings with the broader historical context to demonstrate that that Sarda’s motivations for campaigning for a higher age of marriage was tied to a Hindu nationalist agenda.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.036
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.014
Scholarly communication0.0050.003
Open science0.0010.003
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.200
GPT teacher head0.435
Teacher spread0.235 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2018
Admission routes1
Has abstractyes

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