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Record W2911633505 · doi:10.1177/105268461602600504

Principals’ Technology Leadership

2016· article· en· W2911633505 on OpenAlexaboutno aff
Barbara Brown, Michele Jacobsen

Bibliographic record

VenueJournal of School Leadership · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicTeacher Education and Leadership Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTransformative learningEducational leadershipConceptual frameworkShared leadershipTransformational leadershipPedagogyKnowledge managementSociologyDistributed leadershipLeadership studiesLeadership theoryLeadership stylePsychologyEngineering ethicsPublic relationsPolitical scienceEngineeringComputer scienceSocial science

Abstract

fetched live from OpenAlex

A multifaceted conceptual framework of principals’ technology leadership informed the design of a mixed methods case study exploring leadership practices across three school jurisdictions in Alberta, Canada. Leadership practices of K-12 school principals involved in making school-wide improvements integrating technology were examined The intent of this article is to discuss how the conceptual framework influenced the research process as an interconnection of learning theory based on the learning sciences, transformative knowledge-building pedagogies, and the complexities for school leaders as they cultivate a growth-oriented culture. Findings are related to three key areas: (1) leadership preparation is needed in instructional leadership and technological fluency; (2) online networks can support professional learning; and (3) practitioner–researcher partnerships can support innovation in schools.

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)0.007
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.004
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.452
GPT teacher head0.416
Teacher spread0.036 · 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 designNot applicable
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

Citations23
Published2016
Admission routes1
Has abstractyes

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