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Record W2965887654 · doi:10.4025/jphyseduc.v30i13060

Desenvolvimento técnico-tático: evidências de validade de escalas de medida de conteúdos pedagógicos no esporte

2019· article· pt· W2965887654 on OpenAlexaff
Gabriel Henrique Treter Gonçalves, Marcos Alencar Abaíde Balbinotti, Guy Ginciene, Marcelo Cardoso, Roberto Tierling Klering, Carlos Adelar Abaide Balbinotti

Bibliographic record

VenueJournal of Physical Education · 2019
Typearticle
Languagept
FieldHealth Professions
TopicPhysical Education and Pedagogy
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsHumanitiesPsychologyPhilosophy

Abstract

fetched live from OpenAlex

O objetivo desta pesquisa foi demonstrar as primeiras evidências de validade das escalas de favorecimento ao desenvolvimento de habilidades motoras e estratégico-tático, componentes da Bateria de Testes Gonçalves-Balbinotti de Favorecimento ao Desenvolvimento de Conteúdos Pedagógicos no Esporte Infantojuvenil, a partir da estimação de suas estruturas internas, testagem de suas estabilidades e estimação de suas consistências internas. Uma amostra de 210 treinadores e professores esportivos de 20 a 75 anos, de ambos os sexos, respondeu às escalas referentes ao desenvolvimento de habilidades motoras e estratégico-tático, as quais apresentaram estruturas compostas por três fatores, com saturações significativas (Satf > 0,40) e explicando respectivamente 70,19% e 74,29% da variância total dos construtos. Os resultados relativos ao ajuste do modelo foram, de forma geral, satisfatórios (X2/gl < 1,567; AGFI = 1,000; RMSEA < 0,052; CFI > 0,995). Os resultados do estudo de consistência interna (0,736 < α < 0,908 para os fatores; αHM = 0,869; αET = 0,921) asseguram a precisão das medidas e a confiabilidade de sua utilização aos objetivos a que se propõe. Os resultados respondem aos objetivos central e específicos da pesquisa e indicam a possibilidade da segura utilização das duas escalas.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.270
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.002

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 head0.047
GPT teacher head0.460
Teacher spread0.413 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations3
Published2019
Admission routes1
Has abstractyes

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