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Record W4378386435 · doi:10.5509/2023962323

Moderation of Sarawak Regionalism in Malaysia’s 15th General Election

2023· article· en· W4378386435 on OpenAlexvenueno aff
Ik-Tien Ngu

Bibliographic record

VenuePacific Affairs · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicAsian Studies and History
Canadian institutionsnot available
Fundersnot available
KeywordsRegionalism (politics)Political economyPolitical scienceOpposition (politics)PoliticsAutonomyDemocracyRegional autonomyEliteNationalismGeneral electionCentralized governmentPublic administrationSociologyLaw

Abstract

fetched live from OpenAlex

Compared to Sarawak state elections, in the 2022 general election (GE15) the incumbent Sarawak Parties Alliance (Gabungan Parti Sarawak, GPS) and Sarawak-based opposition parties' campaigns demonstrated a tendency toward a moderate rather than radical autonomy discourse. In GE15, these candidates attempted to reconcile their regionalism with nationalism to stay relevant in the national political landscape. Meanwhile, opposition parties performed better in certain Chinese- and Dayak-majority seats, exposing the limits of the Sarawak autonomy discourse. To explain these patterns, this article locates Sarawak against the backdrop of a centralized Malaysian federal government to clarify the salience of both structural and cultural factors in shaping autonomy claims. It shows the importance of inclusive and democratic institutions, like a general election, in integrating peripheral communities and checking radical regionalism. Further, preceding the general election, the readiness of the federal government to delegate power to the state government effectively secured the state ruling elite's commitment to remain in the national government. However, institutional decentralization is insufficient to calm autonomy claims, as cultural pluralism increasingly underpins Sarawak regionalism. Sarawak's autonomy discourse will not fade away and could radicalize again if the central government holds to exclusive ethnoreligious nationalism.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.970
Threshold uncertainty score0.234

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 head0.022
GPT teacher head0.275
Teacher spread0.253 · 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 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

Citations2
Published2023
Admission routes1
Has abstractyes

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