The Promise and Pitfalls of Government Guidance Funds in China
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it