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Record W7116959170 · doi:10.1080/13632434.2025.2602008

The leadership of innovation in education: findings from an environmental scan

2025· article· en· W7116959170 on OpenAlexaff
Sharon Friesen, Nicola Sum, Rania Sawalhi, Stephen MacGregor, Paul Campbell, Joan M. Conway, Dorothy Andrews, Adelee Penner

Bibliographic record

VenueSchool Leadership and Management · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicTeacher Education and Leadership Studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsGovernment (linguistics)Context (archaeology)Work (physics)Process (computing)

Abstract

fetched live from OpenAlex

Although innovation is widely promoted as essential to educational transformation, definitional ambiguity and a persistent disconnect between policy rhetoric and implementation realities leave educational leaders without the conceptual clarity or systemic support necessary to enact meaningful change. This article presents findings from a global environmental scan that explores how innovation in education is defined, enacted, and constrained through educational leadership. Guided by systematic screening standards, the scan initially identified 147 grey literature sources, of which 94 met the inclusion criteria. Using comparative and thematic analysis, the study examines how educational leadership mediates innovation across diverse governance, policy, and cultural contexts. Three key themes emerged: (1) the enabling and constraining conditions for innovation; (2) the persistent tensions leaders face between top-down mandates and grassroots responsiveness; and (3) the global variation in leadership strategies shaped by sociopolitical and economic conditions. These findings highlight a significant disconnect between policy rhetoric and leadership realities, as well as a lack of definitional clarity and systemic support for innovation. This study calls for more context-responsive, relational, and adaptive leadership frameworks that align local needs with broader reform goals.

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)0.001
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.484
Threshold uncertainty score0.366

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.108
GPT teacher head0.363
Teacher spread0.254 · 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 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

Citations5
Published2025
Admission routes1
Has abstractyes

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