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Record W4393866813 · doi:10.53555/sfs.v10i1.2417

A Comparative Study On The Physical Education Programs Of The Government And Private Schools In Tripura

2023· article· en· W4393866813 on OpenAlexvenueno aff
Mr. Laxmindar Debnath, Surjya Kanta Paul

Bibliographic record

VenueJournal of Survey in Fisheries Sciences · 2023
Typearticle
Languageen
FieldHealth Professions
TopicPhysical Education and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)Private schoolPhysical educationMathematics educationPolitical scienceBusinessPsychology

Abstract

fetched live from OpenAlex

The present research examines the physical education programs of the government and private schools in Tripura. For this study, the researcher has selected 88 nos. of government schools, which are under Vidyajyoti schools, and 28 nos. of private schools (government unaided). 88 nos. of Vidyajyoti schools (government) were selected by using the purposive sampling method: 55 nos. of Vidyajyoti schools, which are located in rural areas, and 33 nos. of Vidyajyoti schools, which are located in urban areas in Tripura. On the other hand, 28 nos. of private schools were selected by the same method: 18 nos. of private schools, which are located in rural areas, and 10 nos. of private schools, which are located in urban areas in Tripura. To obtain the data, the researcher used a questionnaire on the physical education program that was constructed and standardized by Dr. S. K. Paul under the name of S. K. Paul’s Physical Education Program Scale. This scale was administered to the physical education personnel of the rural and urban Vidyajyoti schools and private schools in Tripura. The obtained data was analyzed using descriptive statistics and inferential statistics (t-test). Descriptive statistics implied that the data was more or less normal. And a t-test revealed that there were significant differences in the physical education programs of Vidyajyoti and private schools in Tripura. So, the present study suggests that larger-scale studies are required for more generalization

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score0.173

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.499
GPT teacher head0.502
Teacher spread0.003 · 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 designObservational
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

Citations1
Published2023
Admission routes1
Has abstractyes

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