Bibliographic record
Abstract
With the development of marketing economy and legal system construction, people are paying more and more attention to the transparency, structure and the effectiveness of government finance. Information supplied by Government Budget Accounting provides the best way according to which people supervise and judge the government’s performance. But current Government Budget Accounting has shortcomings in theory foundation. So it’s very urgent to promote reforms on it. This article mainly introduces the difference between the new performance budget and the traditional budget. Key words: Performance budget, Traditional budget, Comparison Resume: Avec le developpement de l’economie de marche et la construction du systeme legal, on prete plus d’attention a la transparence, la structure et l’efficacite des finances gouvernementales. Et les informations fournies par la Comptabilite de Budget du Gouvernement sont importantes, car on peut superviser et juger, d’apres ces informations, la performance du gouvernement. Mais la Comptabilite de Budget du Gouvernement en cours manifeste des defauts dans la fondation theorique. Donc il est urgent d’effectuer la reforme en ce domaine. L’article present introduit principalement les differences entre le nouveau budget de performance et le budget traditionnel. Mots-Cles: budget de performance, budget traditionnel, comparaison
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 | 0.010 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".