BETTING ON (UN)CERTAIN FUTURES: SOCIOTECHNICAL IMAGINARIES OF AI AND VARIETIES OF TECHNO-DEVELOPMENTALISM IN ASIA
Bibliographic record
Abstract
The proliferation of generative artificial intelligence (AI) has prompted the development of comprehensive AI developmental and governance frameworks globally. Yet, existing literature on AI innovation in non-Western societies often overlooks economically advanced but geographically non-dominant societies, instead focusing on large nation-states like China or developing regions in Global South such as South Africa. This paper examines the variegated sociotechnical imaginaries of AI in three Asian developmental societies - Singapore, Hong Kong and Taiwan - addressing two research questions: what are the desired forms of AI development and governance in small-size advanced economies? How does this desired form vary according to the historical, institutional, and geopolitical contexts of these societies? Through discourse analysis of policy documents from the early 2010s to 2024, the paper identifies three imaginaries of techno-developmentalism: Singapore’s cybernetic pragmaticism to legitimize its neoliberal authoritarian rule, Hong Kong’s techno-entrepreneurship in refashioning financial capitalism, and Taiwan’s defensive survival modality against internal socio-economic instability and external threats posed by the rivalry of superpowers. Decision-makers in these societies must establish AI developmental frameworks capable of resource allocation, actor coordination, strategic coupling with the global tech economy, and managing uncertainties in specific AI-centric socio-economic reform. By offering comparative case studies of these Asian societies, this paper contributes to understanding the heterogeneous narratives and practices of AI innovation, moving beyond simplistic narratives trapped in the Global North and South binary.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.049 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".