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Record W3197692716 · doi:10.5509/2021943567

The Politics of Claim-Making in India

2021· article· en· W3197692716 on OpenAlexvenueno aff
Diego Maiorano

Bibliographic record

VenuePacific Affairs · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicPolitics and Conflicts in Afghanistan, Pakistan, and Middle East
Canadian institutionsnot available
Fundersnot available
KeywordsClientelismPoliticsCitizenshipState (computer science)Political scienceGovernment (linguistics)WelfarePublic administrationPolitical economySociologyEconomic growthLawDemocracyEconomics

Abstract

fetched live from OpenAlex

How do Indian citizens access the state?While a standard answer would be "through patronage," three recent books show that clientelism, while important, is just part of the story.Not just passive clients at the mercy of their political patrons, Indian citizens actively engage the state and their representatives to make claims and secure what is due to them.Gabrielle Kruks-Wisner's Claiming the State-Active Citizenship and Social Welfare in Rural India shows how rural dwellers navigate the local government system to access social welfare.Adam Auerbach's Demanding Development: The Politics of Public Goods Provision in India's Urban Slums documents how local political workers make claims on behalf of their neighbours and provide their settlements with essential services.Jennifer Bussell's Clients and Constituents: Political Responsiveness in Patronage Democracies persuasively demonstrates the importance of higher-level representatives in providing assistance to their constituencies.Together, these books not only demonstrate how political the daily life of ordinary citizens is, but also how the Indian state, while far from its Weberian ideal, is much more inclusive than previously thought.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.075
Threshold uncertainty score0.164

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.004
Science and technology studies0.0350.043
Scholarly communication0.0300.005
Open science0.0020.015
Research integrity0.0040.010
Insufficient payload (model declined to judge)0.0050.001

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 head0.018
GPT teacher head0.290
Teacher spread0.273 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2021
Admission routes1
Has abstractyes

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