Bibliographic record
Abstract
Measuring and monitoring the quality of county education is the key to the education quality monitoring. Why should the quality of county education be monitored? What should be monitored and how to monitor the quality of education in a county? These are the questions I will answer in this paper. Based on comparative study, I put forward the different aim and content of education quality monitoring in a county, and conceived three patterns to monitor education quality in a county. Key words : County; Education quality; Monitoring Resume Mesurer et suivre de la qualite de l'education du comte est la cle de la surveillance de la qualite de l'education. Pourquoi devrais la qualite de l'education du comte etre surveilles? Quel devrait etre surveillee et la facon de surveiller la qualite de l'education dans un comte? Ce sont les questions que je vais repondre dans ce papier. Base sur une etude comparative, j'ai avance l'objectif different et le contenu de surveillance de la qualite d'education dans un comte, et a concu trois modeles pour controler la qualite d'education dans un comte. Mots cles: Departement; La qualite de l'education; La surveillance
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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 | 0.016 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".