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Record W4283374130 · doi:10.29173/ijll13

Veteran Teachers’ Perceptions of Principals’ Leadership Influence on School Culture

2022· article· en· W4283374130 on OpenAlexaffabout
Maciej Gebczynski, Benjamin Kutsyuruba

Bibliographic record

VenueInternational Journal for Leadership in Learning · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Military Integration
Canadian institutionsQueen's University
Fundersnot available
KeywordsTransformational leadershipEducational leadershipOrganizational culturePerceptionTeacher leadershipLeadership studiesPsychologyPedagogyTransactional leadershipQualitative researchLeadership styleSchool teachersSociologyPublic relationsPolitical scienceSocial psychologySocial science

Abstract

fetched live from OpenAlex

School leadership and organizational culture both play an influential role in student success and academic achievement. Because school cultures consist of levels that are explicit (easily observable manifestations) and implicit (taken-for-granted, underlying assumptions), veteran teachers usually have deeper understandings of school cultures. This paper describes a qualitative study that examined veteran teachers’ perceptions of school principals’ leadership influence on school culture within the secondary school setting in Ontario. Upon reviewing the relevant literature and methodological underpinnings, we detail key themes from the study: a) effective leadership’s impact on school culture, which aligned with authentic and transformational leadership models; b) ineffective leadership’s impact on school culture, consistent with models of irresponsible leadership; and c) external factors mitigating the influence of school leadership on school culture. The paper concludes with implications for practice and further research.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.109
Threshold uncertainty score0.217

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.189
GPT teacher head0.410
Teacher spread0.220 · 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

Citations2
Published2022
Admission routes2
Has abstractyes

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