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Record W2011256124 · doi:10.1080/14733285.2015.1033614

Educating the nation: shaping student-citizens in Indonesian schools

2015· article· en· W2011256124 on OpenAlexaff
Sarah Moser

Bibliographic record

VenueChildren s Geographies · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicAsian Studies and History
Canadian institutionsMcGill University
FundersNational University of Singapore
KeywordsPerformative utterancePower (physics)SociologyMeaning (existential)State (computer science)IndonesianPerformativityBureaucracyIdentity (music)CeremonyRepetition (rhetorical device)Embodied cognitionPedagogyGender studiesMedia studiesAestheticsPolitical sciencePsychologyLawHistoryArtEpistemologyPolitics

Abstract

fetched live from OpenAlex

Children in Indonesia experience the state in ways that are vastly different from any other citizen. This article explores how the bodies of schoolchildren are a key site for nation-building practices in Indonesia through an examination of two state schools in Riau Islands Province. I investigate the ways in which national identity is inculcated in students through various performances intended to shape the student-citizen, including the wearing of school uniforms, morning national callisthenics and the weekly flag ceremony. Drawing on Judith Butler's concept of performativity, I argue that students’ embodied performances of the nation can be understood as performative in the necessity of repetition or ‘citational practices’, which perpetuate the meaning and maintain the power associated. It is through repetition that meanings embedded in the performances of schoolchildren, such as hierarchy, awareness of a higher bureaucratic power and a sense of belonging to the nation, are perpetuated and normalized to the performers.

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)0.002
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0120.013
Scholarly communication0.0070.003
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.050
GPT teacher head0.318
Teacher spread0.269 · 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

Citations20
Published2015
Admission routes1
Has abstractyes

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