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Record W2923784592 · doi:10.18740/ss27199

The echoes of Ghadar in the Punjabi movement

2017· article· en· W2923784592 on OpenAlexvenueno aff
Sara Kazmi

Bibliographic record

VenueSocialist studies · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicPolitics and Conflicts in Afghanistan, Pakistan, and Middle East
Canadian institutionsnot available
Fundersnot available
KeywordsSubalternPoliticsNationalismDissentConversationSociologyPolitical dissentMarxist philosophyStress (linguistics)Nationalist MovementMovement (music)Gender studiesInterpretation (philosophy)ElitePoetryAestheticsMedia studiesPolitical scienceLinguisticsLawArtPhilosophy

Abstract

fetched live from OpenAlex

This paper sheds light on the creative interpretation of Ghadar’s legacy by the Marxist Punjabi movement in Pakistan, which began in the 1960s. Putting Ghadar di Goonj and Ghadar’s cultural politics into conversation with the work of these contemporary activists sheds light on unexplored facets of both. It unveils how these activists invoke Ghadar to subvert the narrow discourse of “Punjabiyat” and avoid being reduced to an ethno-linguistic and nationalist movement in a country where Punjab remains the dominant region, and allows an appreciation of the politics of language which underpinned Ghadri poetry. The intertwining of these histories of literary dissent reveals how Ghadar’s cultural politics connect with the Punjabi movement through an understanding of Punjabi as a working class language, and a vessel for subaltern consciousness and native traditions of revolt.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0280.039
Scholarly communication0.0100.005
Open science0.0010.009
Research integrity0.0030.008
Insufficient payload (model declined to judge)0.0020.000

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 head0.121
GPT teacher head0.459
Teacher spread0.339 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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