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Concept, Implementation and Characteristics of Teacher Leadership Development in Canada: An analysis based on the "Teacher Learning and Leadership Program" in Ontario

2024· article· en· W6960902326 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Mapping and Diversity in Plants and Animals
Canadian institutionsnot available
Fundersnot available
KeywordsProfessional developmentLeadership developmentProfessional learning communityChinaEducational leadershipTeacher leadershipShared leadershipNeuroleadership

Abstract

fetched live from OpenAlex

With the continuous reform and development of the global education system, leadership has increasingly become one of the core qualities of teachers, bringing a significant influence on their professional development. Since its first implementation, the Teacher Learning and Leadership Program in Ontario, Canada has undergone years of reform and improvement that continuously develops teachers with excellent leadership. In terms of design concept, the Program is committed to reforming the professional development of teachers, enhancing students' academic achievements, and promoting school improvement. In terms of implementation strategy, teachers' leadership is mainly trained through comprehensive application review, screening based project application, and systematic training, presenting the main characteristics of rich and diversified learning modes, flexible and open feedback mechanisms, and comprehensive and complete guarantee systems. Drawing on the experience of the Teacher Learning and Leadership Program, China should expand the coverage of training, build a learning community centered on collaboration and sharing, create an organizational environment to guarantee the training, and effectively cultivate and enhance teacher leadership.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.859
Threshold uncertainty score0.997

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.006
Science and technology studies0.0060.002
Scholarly communication0.0030.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.284
GPT teacher head0.459
Teacher spread0.175 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2024
Admission routes1
Has abstractyes

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