A Development of Academic Management on Small School Model Under The Office of Udonthani Educational Service Area 2
Bibliographic record
Abstract
The purpose of study were ; 1) to determine the Academic Management on \nsmall school Model ; 2) to Develop of Academic Management on small school model \nUnder the office of Udonthani Educational Service Area 2. The research Methodology \nconsisted of four steps ; 1) Analyzing the documentary research ; 2) developing the \nacademic management on small school factors and Indicators with Delphi Technique by \nverifying of 21 experts ; 3) seeking the advices and the feedbacks from nine experts in \nUdonthani using focus group discussion ; 4) evaluating the 110 school Administrators \nopinions in Udonthani. The analysis of the data was accomplished by computation of \npercentage, mean, standard deviation, median and interquartile ranges. \nBased on the findings to the study; it was concluded that: 1) The group of \nexperts represented the three factors were ; a) A development school Record of students ; \nb) A development attribute of student ; c) care forward successes with 15 Indicators. \n2) Based on group discussion, all experts strongly agreed with the used and Integration of three \nfactors of academic management on small school Model. 3) Overall, the at 110 school \ndemonstrations in Udonthani agreed with academic management on small school model \nat high level. 4) A development of academic management on small school model in the \noffice of Udonthani Educational Service Area 2 ; a) A development school Record of \nstudents with 5 indicators ; b) A development attribute of student with 6 indicators ; c) Care \nForward Successes with 4 indicators.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".