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Record W2460745778 · doi:10.1177/0021909616653258

Social Movements and State Repression in India

2016· article· en· W2460745778 on OpenAlexaff
Raju J Das

Bibliographic record

VenueJournal of Asian and African Studies · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSouth Asian Studies and Conflicts
Canadian institutionsYork University
Fundersnot available
KeywordsState (computer science)Political economyMovement (music)Social movementIdeologyDemocracyPoliticsPolitical scienceSociologyCriminologyDevelopment economicsLawEconomics

Abstract

fetched live from OpenAlex

State repression is particularly likely when social movements target property relations that cause ordinary citizens to suffer. Whether these movements are violent, and whether the state is a liberal democracy is a contingent matter. This is illustrated by India’s ‘Maoist movement’ (which is also known as the Naxalite movement because it originated in an area called Naxalbari, located in India’s West Bengal State). Where necessary, sections of this movement use violent methods to fight for justice for aboriginal peoples and peasants. This strategy, which the author, incidentally, does not endorse, has been seen by the state as the greatest internal military threat to it. Such a perception invites state violence. What is often under-emphasized or ignored is that the movement is an economic, political and ideological threat, and not just a military threat, and it is so through its localized alternative developmental activities, and this is also a reason for the state’s violent response to it.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0060.010
Scholarly communication0.0050.001
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.034
GPT teacher head0.339
Teacher spread0.304 · 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 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

Citations12
Published2016
Admission routes1
Has abstractyes

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