Bibliographic record
Abstract
The development of electronic-government is the important field in the informational proceeding. According to China’s situation, we should enhance guidance, definitude the target, make a uniform programming, conformity information resource, strengthen the legislation of e-government, foster the civil servants, in order to insure the successful development of e-government. Key words: E-Government, Information Resource, Management, Standard Resume: Le developpement de l’electronisation des affaires du gouvernement est devenu le domaine le plus important de l’informatisation contemporaine et la focalisation du monde entier . Dans le processus de la mise en oeuvre du systeme de l’electronisation des affaires du gouvernement en Chine , il faut renforcer la direction , determiner l’objectif , elaborer un plan commun , reorganiser les ressources informatiques , legiferer l’electronisation des affaires du gouvernement , mettre l’accent sur la formation des fonctionnaires pour assurer le developpement progressif de l’electronisation des affaires du gouvernement Mots- cles: l’electronisation des affaires du gouvernement , les ressources informatiques , le standard , la gestion 摘 要:電子政務的發展已成為當代資訊化的最重要的領域和全世界關注的熱點。在中國電子政務系統建設和實施過程中，必須加強領導，明確目標，統一規劃，整合資訊資源，加強電子政務的立法，抓好公務員的培訓工作，確保電子政務穩步、健康地向前發展。 關鍵詞:電子政務；資訊資源；管理；標準
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".