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From Sabang to Merauke: Nationalist secession movements in Indonesia

2007· article· en· W2138761324 on OpenAlexaff
David Webster

Bibliographic record

VenueAsia Pacific Viewpoint · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicAsian Studies and History
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNationalismSecessionGrievanceIndependence (probability theory)Context (archaeology)IndonesianCollective identityIdentity (music)DemocracyNationalist MovementPolitical economyIslamPolitical scienceSociologyGender studiesLawHistoryPolitics

Abstract

fetched live from OpenAlex

Abstract: Popular movements in Aceh and Papua seeking separation from Indonesia must be understood in the context of earlier nationalist movements in history, including Indonesia's own movement for independence from the Netherlands. Movements in Aceh and Papua have built a sense of identity, considering themselves to be ‘notion‐states’ even if they are not yet nation‐states. This process parallels Indonesian identity formation in the early twentieth century. Aceh originally combined local, Indonesian and Islamic identities, but intrusion by central government institutions sparked a defensive nationalist reaction, which was stimulated further by uneven economic development and by repressive tactics by the centre. Papua was incorporated into Indonesia by means that led local people to believe they had been denied their right to self‐determination, spurring a historical sense of grievance and a collective identity of shared suffering much like that in Aceh. By the end of Sukarno's Guided Democracy and Suharto's New Order, both territories had passed a point of no return in their nationalism. Repressive tactics have failed to contain aspirations for independence; a new approach based on dialogue is needed.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0070.005
Scholarly communication0.0040.003
Open science0.0000.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.018
GPT teacher head0.314
Teacher spread0.296 · 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 designObservational
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

Citations14
Published2007
Admission routes1
Has abstractyes

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