Analysis on Elective Courses in Ontario and New South Wales State
Bibliographic record
Abstract
PURPOSE This study aims to analyze elective courses in overseas physical education curricula and explore directions to improve the national physical education curriculum.METHODS Physical education curricula from the Ontario Ministry of Education and New South Wales Department of Education, and an administrative announcement book of the 2022 Revised Physical Education Curriculum were collected and analyzed. RESULTSThe Ontario physical education curriculum offers a range of elective subjects that fit students' need to enter universities and colleges.It also has a systematic curriculum flowchart within elective courses.The NSW physical education places importance on learning life skills and offers content-endorsed courses that comprises core studies and optional modules.CONCLUSIONS This study clarified the differences between the learning content of elective subjects and suggested the necessity of developing plans to provide students with effective course path. 서론 서론교육부에서 2021년 11월에 발표한 '2022 개정 교육과정 총론 주요 사항'은 미래 사회 적응에 요구되는 역량 함양, 학습자의 삶과 성장 지원, 교육과정 자율성 및 책무 확대,
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".