The Impact of Democratization, Political Culture, and Diplomatic Isolation on Think-Tank Development in Taiwan
Bibliographic record
Abstract
Taiwan’s landscape of think tanks, despite having emerged during a time of Leninist one-party governance and state-led economic development not unlike that in Mainland China, is today marked by a substantial agency in conducting both research and advocacy. This sets them apart from their counterparts on the mainland. We ask how this development was shaped by Taiwan’s evolution as a political entity, especially its experience of gradual political liberalization and eventual full democratization by the mid-1990s. In its wake, multiparty competition, factionalism, the emergence of a vigorous civil society, and individual interest groups created an environment in which think-tank services were sought by many competing actors, offering a wide array of funding opportunities for policy research. Additionally, a political culture that stresses expertise and the need to conduct unofficial diplomacy often gave think tanks a privileged position within the system, and they served as key agents in conducting the kind of informal diplomacy made necessary by Taiwan’s loss of diplomatic recognition from the 1970s onwards.We further offer an overview of Taiwan’s think-tank landscape, describing major groups (or types) of institutes and briefly portraying especially prominent cases within them. Finally, we provide two detailed case studies to show how these institutes operate in practice, and how the need for unofficial diplomacy and a recent government change have shaped their activities.
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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 | 0.004 | 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 | 0.007 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".