Evaluation of Current Condition of Physical Education Curriculum of Iranian High Schools and the Prescribed Strategies to Improve Its Overall Situation Based on Expertise Ideas and Viewpoints
Bibliographic record
Abstract
The ultimate goal of such inquiry and meticulous investigation is to evaluate the current condition of physical education curriculum of Iranian high schools and the strategies that can be employed in a path of improving its overall situation based on expertise ideas and their total viewpoint is such given pivotal affair. This investigation has been conducted in accordance with pathological phenomenology and sampling with regard of practical and feasible drawn-target and qualitative approach and method. The cited interviews were designated for 15 connoisseurs in the firmament of physical education. The figurative and the content narration of the study has evaluated in compliance with expertise viewpoints and ideas. The total findings and discovered entities as off-springs of expertise ideas in the fields of “fulfilling student’s expectances and their needs and desires”, “attention toward the reals of science, capacity and sight-perspectives”, has been extracted and summarized. The conclusion and overall gains of given investigation manifested that the criteria of high school curriculum were not expedient and appropriate in the fields of target, content, the employed pathological principle of instruction and the given evaluation in-use and it never satisfied the visualized expectance of expertise.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".