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Record W3137486359 · doi:10.1080/14675986.2021.1889471

Examining alternative school leadership practices and approaches: a decolonising school leadership approach

2021· article· en· W3137486359 on OpenAlexafffund
Ann E. Lopez

Bibliographic record

VenueIntercultural Education · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicAfrican cultural and philosophical studies
Canadian institutionsUniversity of TorontoInstitute for Christian Studies
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsEducational leadershipLeadership studiesShared leadershipInstructional leadershipLeadershipPedagogySociologyNarrativeServant leadershipLeadership styleTransactional leadershipNeuroleadershipNarrative inquiryPublic relationsPolitical science

Abstract

fetched live from OpenAlex

Much of the research on school leadership emanates from western contexts, and scholars in these countries have great influence on educational leadership theory, policy and practice. Leadership knowledge, theorising and practices can no longer be generated predominantly by scholars from the North. Grounded in a framework of decolonising educational leadership, this paper examines the practices and approaches of school leaders in Kenya. Utilising a narrative inquiry methodological approach, the leadership perspectives and practices of secondary school principals were examined. Findings reveal convergences and departures with contemporary leadership theories and practices, different approaches to student engagement and success, desire for a more consistent approach to school leadership preparation and complexities navigating socio-cultural issues. Findings of this research have implications for leadership preparation and development in the Global South and broader educational leadership discourse.

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)0.011
metaresearch head score (Gemma)0.009
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.002
Science and technology studies0.0080.016
Scholarly communication0.0070.007
Open science0.0010.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.602
GPT teacher head0.395
Teacher spread0.207 · 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

Citations16
Published2021
Admission routes2
Has abstractyes

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