Embracing Scientific Decision Making: The Rise of Think-Tank Policies in China
Bibliographic record
Abstract
The literature has traditionally regarded the rise and fall of Chinese think tanks as a product of the relaxation or tightening of control over the ideological domain by the Communist Party of China (CPC). However, since 2013, think tanks in China have continued to prosper in spite of the general perception that the ideological domain became more restricted. This article explains the reasons for, and consequences of, the rise of key think-tank policies and the construction of “New-Type Think Tanks with Chinese Characteristics” (NTTTCC). We argue that the success of Chinese think tanks has been driven primarily by greater official recognition of their value, due to increasingly complex domestic and international problems stemming from a fragmented decision-making system. We further argue that the rise of think-tank policies in China can be attributed to a long history of interactions among multiple internal and external actors, which in turn, opened the “window of opportunity” for a new policy agenda. Consequently, by late 2015, the new policies led to the selection of twenty-five “pilot high-end think tanks” and the establishment of the management system of think tanks in China.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".