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Record W2371621238

Comparative Research on the Contents of Physical Education and Health Course in Foreign Middle Schools

2007· article· en· W2371621238 on OpenAlexaboutno aff
Dou Li

Bibliographic record

VenueBeijing Tiyu Daxue xuebao · 2007
Typearticle
Languageen
FieldHealth Professions
TopicPhysical Education and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsPhysical educationFlexibility (engineering)Lifelong learningDiversification (marketing strategy)EntertainmentUnificationHealth educationMedical educationPublic relationsPsychologyPolitical scienceEconomic growthMathematics educationMarketingPedagogyHealth careMedicineBusinessManagementEconomicsComputer science
DOInot available

Abstract

fetched live from OpenAlex

This paper comparatively analyzes and researches the contents of physical education and health course in England,USA,New Zealand,Canada,and Japan. The results show that the common development trend of the contents of physical education and health course in foreign countries is to mark off the contents of P.E.according to the learning fields,attach importance to the contents of fitness and motor skills,show the flexibility and selectivity of the contents,fit with the individual differences,combine P.E.with health teaching,emphasize the lifelong P.E.,and strengthen its contact with life.The inspiration for Chinese curricular contents' reform is that the contents should meet the needs of society and scientific development,pay attention to students' needs,care for the individual differences,emphasize the functions of strengthening students' health,combine P.E.with health education,strengthen the education of P.E.and health knowledge,show diversification,flexibility and local characteristics,reflect the needs of lifelong P.E.,and pay attention to the unification of athletics,body building and entertainment.In the learning fields the samples of knowledge and skills should be provided to meet the demands of contents.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.773
Threshold uncertainty score0.454

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.632
GPT teacher head0.649
Teacher spread0.017 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2007
Admission routes1
Has abstractyes

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