Betting on (un)certain futures: sociotechnical imaginaries of AI and varieties of techno-developmentalism in Asia
Bibliographic record
Abstract
The rise of generative artificial intelligence (AI) has prompted governments worldwide to formulate national strategies. However, existing research on AI innovation discourse remains centered on dominant economic and technological powers, or Global South critiques of data colonialism and infrastructural imperialism. This paper examines how three geographically non-dominant developmental Asian societies, namely Singapore, Taiwan and Hong Kong, construct AI-driven futures through the lens of sociotechnical imaginaries. Using interpretive discourse analysis of national policy documents, the study identifies a shared techno-developmental imaginary that frames AI as an inevitable yet necessary force for socio-economic survival, focused on enhancing ‘smartness’ through computational power while managing associated uncertainty. Despite this convergence, each society articulates variegated techno-developmental orientations embedded in specific historical, institutional and geopolitical contexts. Singapore advances cybernetic pragmatism aligned with authoritarian-technocratic governance; Taiwan promotes AI as a defensive modality for economic nationalism and democratic sovereignty; and Hong Kong pursues techno-entrepreneurial intermediation within Chinese state capitalism. These imaginaries shape how governing authorities coordinate institutional actors, manage global positioning and geopolitical risks, and mobilize resources across uneven AI production networks. The analysis contributes to STS, communication studies and critical AI studies by examining how global AI governance is locally imagined and legitimized, revealing the layered, contingent, and contested nature of techno-futures.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.045 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".