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Record W1870822093 · doi:10.22230/ijepl.2015v10n5a572

Principals’ moral agency and ethical decision-making: Towards transformational ethics

2015· article· en· W1870822093 on OpenAlexafffundvenueabout
Sabre Cherkowski, Keith Walker, Benjamin Kutsyuruba

Bibliographic record

VenueInternational Journal of Education Policy and Leadership · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsUniversity of SaskatchewanQueen's UniversityUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsTransformational leadershipAgency (philosophy)Ethical decisionMoral agencyPublic relationsEngineering ethicsEthical leadershipSociologyPolitical sciencePsychologySocial psychologySocial scienceEngineering

Abstract

fetched live from OpenAlex

This descriptive study of the ethical decision-making among a group of Canadian principals provides a rich portrait of how and why principals engage their moral agency through their decision-making processes. Using a leadership responsibility framework linking moral agency and transformational leadership, the researchers found that: modeling moral agency is important for encouraging others to engage their own moral agency in the best interests of all children; despite efforts to engage in collaborative decision-making, principals are often faced with the reality that they are the one to absorb the cost of the decisions; and principals tend to engage less often in transformational aspects of leadership as part of the decision-making process. More research is needed to understand how school leaders can engage more often and more substantially in transformational leadership among their teachers and staff and how they build moral agency capacity in their 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)0.013
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.457
Threshold uncertainty score0.908

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.026
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0230.026
Scholarly communication0.0110.003
Open science0.0010.005
Research integrity0.0010.006
Insufficient payload (model declined to judge)0.0020.000

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 head0.337
GPT teacher head0.490
Teacher spread0.153 · 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 designTheoretical or conceptual
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

Citations48
Published2015
Admission routes4
Has abstractyes

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