The Promise and Pitfalls of Government Guidance Funds in China
Bibliographic record
Abstract
Abstract In 2005, the Chinese government deployed a new financial instrument to accelerate technological catch-up: government guidance funds (GGFs). These are funds established by central and local governments partnering with private venture capital to invest in state-selected priority sectors. GGFs promise to significantly broaden capital access for high-tech ventures that normally struggle to secure funding. The aggregate numbers are impressive: by 2021, there were more than 1,800 GGFs, with an estimated target capital size of US$1.52 trillion. In practice, however, there are notable gaps between policy ambition and outcomes. Our analysis finds that realized capital fell significantly short of targets, particularly in non-coastal regions, and only 26 per cent of GGFs had met their target capital size by 2021. Several factors account for this policy implementation gap: the lack of quality private-sector partners and ventures, leadership turnover and the inherent difficulties in evaluating the performance of GGFs.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".