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Record W4312043060 · doi:10.29173/ijll22

School leadership standards and graduate education: Instructional negotiations of theory, practice, and policy regulation.

2022· article· en· W4312043060 on OpenAlexaffabout
Ronna Mosher, Lori Pamplin, Nadia Delanoy, Barbara Brown

Bibliographic record

VenueInternational Journal for Leadership in Learning · 2022
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsNegotiationPolitical scienceNormativeDialogicEducational leadershipPedagogyPublic relationsLeadership studiesStandardizationSociologyLeadership style

Abstract

fetched live from OpenAlex

The presence of school leadership standards in graduate education has come to influence the scope and content of leadership programs, highlighting tensions between political, practical, and scholarly views of leaders and leadership. This paper reports on a study of instructional practices within a graduate program in educational leadership connected to the Alberta Leadership Quality Standard to explore how instructors, as policy actors, encounter leadership standards not just as policies of compliance but of possibility. We interpret interview data from three faculty members through the lens of policy enactment to understand how their instruction negotiated relationships of theory and practice and how they negotiated the policy-based regulatory discourses associated with school leadership standards. Working between images of policy standards as text and discourse, findings show instructors engaged in dialogic commitments that help students develop practical and scholarly competencies while displacing the authority of standards, recontextualizing the standardization of leadership, and displacing the standards’ normative gaze.

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)0.024
metaresearch head score (Gemma)0.045
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: none
Teacher disagreement score0.035
Threshold uncertainty score0.199

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.045
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.042
Scholarly communication0.0140.005
Open science0.0010.007
Research integrity0.0030.007
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 head0.401
GPT teacher head0.528
Teacher spread0.127 · 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

Citations1
Published2022
Admission routes2
Has abstractyes

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