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Record W2965138955 · doi:10.1017/s0003975619000080

Violence as a Tactic of Social Protest in Postcolonial India

2019· article· en· W2965138955 on OpenAlexaff
Kristin Plys

Bibliographic record

VenueEuropean Journal of Sociology · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicCommunism, Protests, Social Movements
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsImprisonmentTrade unionPolitical sciencePoliticsOpposition (politics)DemocracyAuthoritarianismResistance (ecology)State (computer science)Political economySocial movementLawCriminologySociologyEconomics

Abstract

fetched live from OpenAlex

Abstract In March 1974, trade union leader and Chairman of the Socialist Party of India, George Fernandes, formed a new independent trade union of railway workers and then led a massive nation-wide strike lasting about a month. Two years later—March 1976—Fernandes was arrested as the principal accused in the Baroda Dynamite Conspiracy Case, a plot to bomb strategic targets in New Delhi in resistance to Indira Gandhi’s authoritarian rule. How did George Fernandes’ political work change over these two years—from engaging in traditional trade union movement tactics during the Railway Workers’ Strike in 1974 to being the ringleader of a plan to bomb strategic targets in resistance to the postcolonial state? Why would an activist who advocated non-violent social movement tactics change strategies and end up leading a movement that primarily uses violent tactics? I argue that in its violent repression of the Railway Workers’ Strike and its illegal imprisonment of the strike’s leaders, Indira Gandhi’s administration demonstrated to Fernandes and other opposition party leaders that there was no room for a peaceful solution to the ever increasing social conflict of early 1970s India. Therefore, when Gandhi instated herself as dictator, longstanding advocates of satyagraha believed that symbolic violence against the state was the tactic most likely to lead to the restoration of democracy in India.

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)0.004
metaresearch head score (Gemma)0.001
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 score0.491
Threshold uncertainty score0.493

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.017
GPT teacher head0.318
Teacher spread0.301 · 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
Published2019
Admission routes1
Has abstractyes

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