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Record W1792430135 · doi:10.7591/9781501719448-001

Introduction: State in Society in Indonesia

2019· article· en· W1792430135 on OpenAlexaff
Gerry van Klinken, Joshua Barker

Bibliographic record

VenueCornell University Press eBooks · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicAsian Studies and History
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsState (computer science)PoliticsDemocracyPolitical scienceScholarshipSociologyPolitical economyGeographyLaw

Abstract

fetched live from OpenAlex

A major realignment is taking place in the way we understand the state in Indonesia. New studies on local politics, ethnicity, the democratic transition, corruption, Islam, popular culture, and other areas hint at novel concepts of the state, though often without fully articulating them. This book aims to capture some dimensions of this shift. One reason for the new thinking is a fresh wind in state studies more generally. People are posing new kinds of questions about the state, and they are developing new methodologies to answer them. Another reason for this shift is that Indonesia itself has changed, probably more than most people recognize. It looks more democratic, but also more chaotic and corrupt, than it did during the militaristic New Order of 1966–98. This book consists of case studies from many different settings around the archipelago. The studies focus on various types of state representatives, such as village heads, informal slum leaders, district heads, and parliamentarians. They explore the spaces and settings where the state is evident and where it is discussed: coffee houses, hotel lounges, fishing waters, and streetside stalls. They investigate state authority, both as a set of actual practices and as an image of what the state “ought” to be. The case studies, and the broader trend in scholarship of which they are a part, allow for a new theorization of the state in Indonesia that more adequately addresses the complexity of political life in this vast archipelago nation.

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)0.000
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: Empirical · Consensus signal: none
Teacher disagreement score0.890
Threshold uncertainty score0.729

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 studies0.0000.000
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 head0.019
GPT teacher head0.201
Teacher spread0.182 · 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 designNot applicable
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

Citations5
Published2019
Admission routes1
Has abstractyes

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