On the Improvement of the Administrative Reconsideration Committee System of China: From the Quasi-judicial Perspective
Bibliographic record
Abstract
In China, the citizens can get administrative remedies through the administrative reconsideration, administrative litigation and complaint letters and visits. Compared with the lawsuits, the administrative reconsideration system as a most important way of the remedies is convenient, effective and free. Compared with the complaint letters or visits, the administrative reconsideration also has the guarantee of a legal procedure. However, in real life, the phenomenon is “Many people like the complaint letters and visits, some people like the lawsuits and a few people like the reconsideration.” So the administrative reconsideration system does not give full play to its advantage, and the key reason is that the administrative reconsideration system tends to have the more administrative character in our nation, contrary to the international trend of approaching the more judicial reconsideration, so that the credibility of the administrative reconsideration has been questioned, and citizens are not willing to settle disputes through the administrative reconsideration. In order to solve these problems, the author puts forward suggestions on improving our nation’s administrative reconsideration system in the background of the quasi-judicial system of the administrative reconsideration.
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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".