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Record W4405309620 · doi:10.5509/2025981-art2

Education, Control, and the Knowledge Economy in Southeast Asia’s Hybrid Regimes

2024· article· en· W4405309620 on OpenAlexvenueno aff
Hui-Yuan Neo

Bibliographic record

VenuePacific Affairs · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSocioeconomic Development in Asia
Canadian institutionsnot available
Fundersnot available
KeywordsSoutheast asiaControl (management)Knowledge economyEconomyPolitical scienceEconomic systemEconomicsAncient historyHistoryManagement

Abstract

fetched live from OpenAlex

Many hybrid regimes are embracing economic upgrading as an engine of sustainable growth. Several of them have also successfully implemented economic liberalization while maintaining political stability. How do hybrid regimes achieve the goals of economic transformation and political stability simultaneously? I argue that one way they achieve this feat is by embedding institutional arrangements within partnership agreements between knowledge-based economic organizations and the state. Hybrid regimes grant these organizations broad autonomy to develop according to economic demands. However, when necessary, they can also draw upon these institutional arrangements to dexterously respond to shifting national interests. I apply this theoretical framework to international branch campuses (IBCs) in the hybrid regimes of Singapore and Malaysia. Leveraging archival materials, I examine the cases of Yale-NUS College in Singapore and the University of Nottingham Malaysia. I show that these IBCs have generally enjoyed wide-ranging autonomy in terms of course design and student activities. However, hybrid regimes can still activate institutional arrangements, such as state funding and mandatory course offerings, to (re)align IBC activities with dynamic regime needs. This research speaks to the political economy and political control literature to examine how hybrid regimes achieve economic liberalization without also allowing political liberalization.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.975
Threshold uncertainty score0.778

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.009
GPT teacher head0.262
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 designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations0
Published2024
Admission routes1
Has abstractyes

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