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Record W4388499115 · doi:10.1080/15295036.2023.2265995

India’s internet shutdowns as biopolitics: The formation of political will and opinion through collective action under attack

2023· article· en· W4388499115 on OpenAlexaff
Sananda Sahoo

Bibliographic record

VenueCritical Studies in Media Communication · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Theology and Sovereignty
Canadian institutionsWestern University
Fundersnot available
KeywordsDissentThe InternetPoliticsScholarshipSociologyBiopowerDemocracyLawMarketplace of ideasCollective actionPolitical sciencePublic opinionAction (physics)Government (linguistics)CensorshipLaw and economics

Abstract

fetched live from OpenAlex

During the 2020–2021 farmers’ movement in India, the central government argued that its temporary internet shutdowns in specific areas associated with the protest aimed to prevent law and order violations. Scholars have analyzed internet shutdowns as a method to control speech, which amounts to an infringement on the freedom of speech and expression. Additional studies have pointed out that such shutdowns directly interfere with the right to assembly. This paper adds to the existing scholarship by positing that internet shutdowns during mass protest movements interfere with a fundamental democracy-related value of the right to assembly, which, to borrow Salát’s phrase, is “the formation of political will and opinion.” Drawing on Foucault’s concept of biopolitics, the paper argues that shutdowns associated with collective protest should also be understood as an interference in the formation of political will and opinion by trying to manage the circulation of information and control a particular behavior, that is, dissent.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.019
Scholarly communication0.0070.004
Open science0.0000.004
Research integrity0.0010.003
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 head0.260
GPT teacher head0.495
Teacher spread0.234 · 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
Published2023
Admission routes1
Has abstractyes

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