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Record W4406847857 · doi:10.5539/ies.v18n1p102

Development of Strategies for Sustainable Development of Chinese Dance Teacher Leadership in Shandong Province

2025· article· en· W4406847857 on OpenAlexvenueno aff
Xi Su, Phatchareephorn Bangkheow, Chollada Pongpattanayothin, Sunet Thaweethawornsawat

Bibliographic record

VenueInternational Education Studies · 2025
Typearticle
Languageen
FieldPsychology
TopicDiversity and Impact of Dance
Canadian institutionsnot available
Fundersnot available
KeywordsDanceSustainable developmentDance educationInstructional leadershipPedagogyPsychologySociologyMathematics educationPolitical scienceEducational leadershipVisual arts

Abstract

fetched live from OpenAlex

The objectives of this research were 1) to study the current situation and supporting factors that enhance the sustainable development of Chinese dance teacher leadership in Shandong Province, 2) to develop the strategies for sustainable development of Chinese dance teacher leadership in Shandong Province, and 3) Evaluate the feasibility of the strategies for sustainable development of Chinese dance teacher leadership in Shandong Province. The sample group of this research consisted of 331 teachers for questionnaires and 12 experts for interview who worked for Chinese Dance in Shandong Province and were sampled through random clusters. The research instruments included 1) questionnaires, 2) interviews, and 3) evaluation forms. The data analysis statistics were percentages, mean, standard deviations, and content analysis. The results revealed the following: 1) The current situation and supporting factors that enhance the sustainable development of Chinese dance teacher leadership were moderate. 2) The strategies for enhancing the sustainable development of Chinese dance teacher leadership include 7 aspects: Professional quality, organizational mechanism, evaluation system, resource support, teacher training, discipline development plan, and international exchange and cooperation. The adaptability and feasibility evaluation results of the strategies implementation were high at the highest level.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.166
Threshold uncertainty score0.323

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.134
GPT teacher head0.421
Teacher spread0.287 · 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
Published2025
Admission routes1
Has abstractyes

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