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Record W3000017101 · doi:10.3390/rel11010034

Interrogating Gender in Sikh Tradition and Practice

2020· article· en· W3000017101 on OpenAlexaff
Satwinder Kaur Bains

Bibliographic record

VenueReligions · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicSouth Asian Studies and Diaspora
Canadian institutionsUniversity of the Fraser Valley
Fundersnot available
KeywordsFaithIdentity (music)SociologyRepresentation (politics)Gender studiesConsistency (knowledge bases)Code (set theory)Expression (computer science)Order (exchange)AestheticsEpistemologyLawPhilosophyPolitical scienceSet (abstract data type)

Abstract

fetched live from OpenAlex

In contemporary Sikh society, what we consider religious is constantly being challenged, but for Sikhs, what remain constant are Sikhi’s sacred texts—they continue to be the paramount teacher and guide. Within this consistency, I ask the question: how can Sikh feminist ideas of representation and identity find expression in response to our understanding/practice of our faith, our institutions, and of the everyday Sikh symbols? This paper critically examines the gendered nature of the Guru Granth, practices within the gurdwaras, and focuses on a part of the Rahit Maryada (Code of Conduct) as an area of exploration in the understanding of the everyday ascribed five symbols of Sikhi (punj kakar) through a feminist lens. I undertake this in order to gain a gendered appreciation of how the scriptures, religious institutions, and the articles of faith resonate with the feminine.

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)0.008
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0110.072
Scholarly communication0.0080.007
Open science0.0010.008
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.124
GPT teacher head0.275
Teacher spread0.151 · 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 designQualitative
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

Citations8
Published2020
Admission routes1
Has abstractyes

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