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Record W3161180310 · doi:10.1017/9781108954266

Colonial Institutions and Civil War

2021· book· en· W3161180310 on OpenAlex
Shivaji Mukherjee

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCambridge University Press eBooks · 2021
Typebook
Languageen
FieldSocial Sciences
TopicSouth Asian Studies and Conflicts
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsInsurgencyColonialismFrontierTypologyEthnic groupPolitical scienceGeographyPolitical economyInequalityDevelopment economicsSociologyLawEconomicsPoliticsArchaeology

Abstract

fetched live from OpenAlex

What explains the peculiar spatial variation of Maoist insurgency in India? Mukherjee develops a novel typology of colonial indirect rule and land tenure in India, showing how they can lead to land inequality, weak state and Maoist insurgency. Using a multi-method research design that combines qualitative analysis of archival data on Chhattisgarh and Andhra Pradesh states, Mukherjee demonstrates path dependence of land/ethnic inequality leading to Maoist insurgency. This is nested within a quantitative analysis of a district level dataset which uses an instrumental variable analysis to address potential selection bias in colonial choice of princely states. The author also analyses various Maoist documents, and interviews with key human rights activists, police officers, and bureaucrats, providing rich contextual understanding of the motivations of agents. Furthermore, he demonstrates the generalizability of his theory to cases of colonial frontier indirect rule causing ​ethnic secessionist insurgency in Burma, and the Taliban insurgency in Pakistan.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.817
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.037
GPT teacher head0.247
Teacher spread0.210 · 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