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Record W4402074842 · doi:10.7454/mjs.v29i1.13570

From A Nation of Quarter-Humans to that of A Proud People: Technology Transfer and Nationalism in Indonesia (1951-1998)

2024· article· en· W4402074842 on OpenAlexaboutno aff

Bibliographic record

VenueMASYARAKAT Jurnal Sosiologi · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicAsian Studies and History
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)NationalismSociologyMedia studiesGender studiesSocial sciencePolitical scienceHistoryLawPoliticsArchaeology

Abstract

fetched live from OpenAlex

A series of technology transfer programs took place after Indonesia became fully independent, from 1951 until the fall of the New Order (1998). This article unpacks the ways the authoritarian New Order government utilize nationalist discourses, reproduced in the postcolonial context. These programs were framed as an antithesis to the negative psycho-social aspects of Dutch colonialism as a mean of postcolonial national integration, and as a strategy to make Indonesia’s position more equal to the more developed countries. At the same time, the technology transfer programs relied heavily on the high capacity of the authoritarian state. This study asserts that New Order demonstrates a case of a high capacity authoritarian state that utilized the success of the technology transfer programs, along with nationalist discourses, to legitimize its power. This article expands on the arguments of the previous studies that focus on the strong capacity of the state in promoting the technology transfer. The previous studies tend to neglect the post-colonial context (including the reproduction of the discourses of nationalism) in technology transfer program.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.562
Threshold uncertainty score0.416

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 head0.020
GPT teacher head0.281
Teacher spread0.261 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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