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Record W2168242029 · doi:10.1177/1741143207075391

Ethical Leadership in Canadian School Organizations

2007· article· en· W2168242029 on OpenAlexaffabout
Lyse Langlois, Claire Lapointe

Bibliographic record

VenueEducational Management Administration & Leadership · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsSociologyContext (archaeology)Ethical codeEconomic JusticeDeontological ethicsIdentity (music)Ethical leadershipPedagogyPolitical sciencePublic relationsLawAesthetics

Abstract

fetched live from OpenAlex

This study, which was sponsored by the Social Sciences and Humanities Research Council of Canada, was conducted in French-language minority schools in seven Canadian provinces: British Columbia, Alberta, Manitoba, Ontario, New Brunswick, Prince Edward Island and Nova Scotia. Using an open-ended interview guide, 47 principals were asked about the tensions and possibilities relating to ethical leadership in linguistic minority contexts. Results show how the presence of an ethic of critique is strongly rooted in a context of struggle for the protection of a French-speaking identity as well as linguistic and cultural survival. Participants' number of years experience as school principals significantly influences their ethical posture. New school principals use only one ethic, the ethic of justice, whereas seasoned principals use a consolidated ethical framework which includes the ethic of care and the ethic of critique. An emerging professional ethic is also observed, built both on each person's ethical nature and his or her life experience. Most importantly, this type of professionalism, very much nurtured by relationships, is auto-regulated rather than externally regulated, as with a deontology code.

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)0.009
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.883
Threshold uncertainty score0.850

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0390.011
Scholarly communication0.0070.001
Open science0.0020.005
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.153
GPT teacher head0.381
Teacher spread0.227 · 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 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

Citations36
Published2007
Admission routes2
Has abstractyes

Explore more

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