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Record W4396710996 · doi:10.1353/jaas.2024.a926985

The Elasticity Of Caste In The Sikh Diaspora: Jat Cool and Caste Masculinities in the Pacific Northwest

2024· article· en· W4396710996 on OpenAlexaboutno aff

Bibliographic record

VenueJournal of Asian American Studies · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicSouth Asian Cinema and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsCasteDiasporaGeographySociologyGender studiesEthnologyPolitical science

Abstract

fetched live from OpenAlex

Abstract: This article examines the transnational dimensions of caste and gender in the Punjabi Sikh diaspora of the Pacific Northwest. I explore how Jats (a landowning caste from Punjab, India) have positioned themselves at times as superior to Chamars (Punjabi Dalits or caste oppressed peoples) in the US-Canada borderland diaspora. Though Sikhism is a religion founded on anti-caste origins, the simultaneous repudiation of caste and celebration of Jat pride paradoxically illustrates structures of caste within the religion. The article unsettles the ways in which Jat men in the diaspora can be implicated in Jat pride and Jat cool: a social currency intertwined with popular culture and social media that reveals a particular caste masculinity. While not all Jats engage with Jat pride, and in fact many are involved in anti-caste praxis, it is important to situate the pervasiveness of these hierarchical ideologies within intra-Sikh communities to understand the permutations and stickiness of caste within the diaspora. I build on Asian American approaches to theorizing caste, youth cultures, and notions of “cool” to ultimately reveal how caste, rather than fixed or natural, is an elastic concept that is contingent upon how it is deployed within Sikh diasporic geographies and temporalities.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.103
Threshold uncertainty score0.204

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.005
Scholarly communication0.0020.001
Open science0.0000.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.025
GPT teacher head0.266
Teacher spread0.240 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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