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Record W3026353415 · doi:10.1177/0892020620927415

School leaders as change agents: Do principals have the tools they need?

2020· article· en· W3026353415 on OpenAlexaff
Karen Acton

Bibliographic record

VenueManagement in Education · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicTeacher Education and Leadership Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAccountabilityPublic relationsPerceptionProfessional developmentEducational leadershipProcess (computing)PsychologyInstructional leadershipTeacher leadershipPedagogyPolitical science

Abstract

fetched live from OpenAlex

In the current climate of accountability and calls for school improvement, principals are dealing with unceasing demands to implement new educational reforms. Yet do school leaders feel equipped to implement these mandates? This study investigated the perceptions of experienced elementary principals on whether they felt prepared to be effective change agents. Findings showed that principals felt they had received very little professional development on how to be a leader of change. Instead, their professional learning as change agents occurred through on the job experience and networking with trusted colleagues. This resulted in knowledge gaps in principals’ understanding of the change process. School leaders bear the responsibility of implementing change, yet principals suggest that reforms would see increased success if they were a shared responsibility with district leaders. Insights from experienced principals may help guide improved professional learning practices to provide educational leaders with the necessary skills to lead effective school improvement.

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)0.013
metaresearch head score (Gemma)0.038
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.038
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0060.005
Scholarly communication0.0100.010
Open science0.0010.004
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0050.003

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 head0.358
GPT teacher head0.442
Teacher spread0.084 · 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 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

Citations89
Published2020
Admission routes1
Has abstractyes

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