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Record W2955056794 · doi:10.1080/13603124.2019.1623922

Understanding the relationship between transformational leadership and collective teacher efficacy in Chinese primary schools

2019· article· en· W2955056794 on OpenAlexaff
Peng Liu, Ling Li, Jianping Wang

Bibliographic record

VenueInternational Journal of Leadership in Education · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicTeacher Education and Leadership Studies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsTransformational leadershipCollective efficacyShared leadershipPsychologyCompetence (human resources)Context (archaeology)Instructional leadershipCollective leadershipPath analysis (statistics)Self-efficacyTransactional leadershipPedagogyEducational leadershipSocial psychologyPolitical scienceChina

Abstract

fetched live from OpenAlex

The purpose of this article is to understand the relationship between transformational leadership and collective teacher efficacy in the Chinese primary school context using quantitative research methods. This article also examines the transferability of transformational leadership and its effects in a high power distance culture. The main research question is, ‘To what extent can transformational leadership explain the variance in collective teacher efficacy in Chinese primary schools?’ The results of path analysis show that managing the instructional program has a significant effect on group competence (a dimension of collective efficacy) and that setting direction affects another dimension of collective efficacy, task analysis. When collective teacher efficacy is treated as a single variable, setting direction and managing the instructional program have relatively significant effects on collective teacher efficacy. This study adds to the literature on the relationship between transformational leadership and collective teacher efficacy in the Chinese primary school context. It advances knowledge of educational management and facilitates the understanding of cross-cultural leadership phenomena.

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)0.002
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 score0.226
Threshold uncertainty score0.564

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.398
GPT teacher head0.422
Teacher spread0.024 · 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

Citations26
Published2019
Admission routes1
Has abstractyes

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