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Contemporary Adivasi poetry in Maharashtra: A conversational voice of dissent

2025· article· en· W4407909105 on OpenAlexaboutno aff
Shubhangi Nitin Jarandikar

Bibliographic record

VenueInternational Journal of Research in English · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSouth Asian Studies and Conflicts
Canadian institutionsnot available
Fundersnot available
KeywordsDissentPoetryLiteratureCommunicationLinguisticsArtSociologyPolitical sciencePhilosophyLaw

Abstract

fetched live from OpenAlex

The process of bringing indigenous voices at the international level started around the 1940s. But the results could be apparent around the ’60s and the ’70s. There emerged an international body called World Council for Indigenous Peoples in the year 1975. It was a consequence of the conference of the indigenous peoples held in Canada. Due to this conference, the contacts between the indigenous people became truly trans-national. It gave indigenous minorities a platform to share their experiences and put forth their ideas and thoughts about their status. The participation in such conferences by the indigenous people created a new awareness among these communities that paved the way for the Indigenous Literature in particular. Hence, the 1960s and the ’70s are assumed to be the emerging years of the indigenous literatures in the world. The term Aborigine communities is also one of the terms used to address the indigenous people. For the last fifty years, the indigenous literary figures have been contributing a lot to expose the malaise of exploitation and its brutal effects on their communities and cultures. The present research article intends to explore major concerns of Indigenous poetry written in Maharashtra and its distinctive poetic form used to communicate these concerns. In India, in the present scenario where religious, communal and caste based identities have become more sensitive, where violence is erupted due to the ethnic identities of the people it is ingenious to listen to the voices and perspective of these communities.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.422
Threshold uncertainty score0.355

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.084
GPT teacher head0.454
Teacher spread0.369 · 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 designObservational
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
Published2025
Admission routes1
Has abstractyes

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