Bibliographic record
Abstract
Public finance matters. It matters for sustained economic growth. It matters for economic stability. It matters for the distribution of income and wealth. It matters for the delivery of such basic services as education and health. It matters for political stability. These statements are as true in China as in any country. What differentiates China from other countries is not that its development is magically unrelated to what its public sector does and how it is financed but rather that the key to its public finance system lies in intergovernmental fiscal relations. Unless China begins to tackle more systematically the serious problems that have emerged in the finances of its various levels of subnational government, the problems to which the present unsatisfactory system give rise will over time increasingly distort resource allocation, increase distributional tensions, and in all likelihood slow down the impressive recent growth of the Chinese economy. These statements may seem strong but as we show in this chapter, the evidence on hand – although far from fully satisfactory, given the lack of solid and reliable information on the size and nature of China's real fiscal system – is consistent with this pessimistic reading. China's fiscal and – in time – economic future rests, to a greater extent than generally seems to be understood, on the success achieved in strengthening and extending recent ad hoc reforms to key aspects of its fiscal system within a more consistent and purposive framework.
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".