MétaCan
Menu
Back to cohort
Record W7124707881 · doi:10.2478/jelpp-2025-0008

Indigenous leadership in a non-Indigenous space A leadership story

2025· article· en· W7124707881 on OpenAlexaboutno aff
Nicole Brouwer

Bibliographic record

VenueJournal of Educational Leadership Policy and Practice · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousDecolonizationContext (archaeology)ScholarshipSpace (punctuation)Leadership studiesProject commissioningColonialismConceptual framework

Abstract

fetched live from OpenAlex

Abstract This article explores the leadership journey of an Indigenous leader working within a non-Indigenous institutional context in Canada, focusing on the ongoing processes of decolonization and reconciliation. Grounded in the Calls to Action of the Truth and Reconciliation Commission of Canada, the article examines how Indigenous leadership principles are enacted in environments shaped by colonial structures and assumptions. Attention is given to the systemic, relational and personal obstacles that challenge effective leadership, including resistance to change, cultural misalignment, and the emotional labour associated with reconciliation work. The Medicine Wheel is employed as both a conceptual framework and a reflective tool, illustrating its role in guiding holistic, ethical and relational approaches to leadership and organizational transformation. By situating Indigenous ways of knowing, being, and doing at the centre of the analysis, this article contributes to emerging scholarship on Indigenous leadership in non-Indigenous spaces and offers insights for educators, administrators, and policymakers engaged in meaningful decolonization and reconciliation efforts.

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)0.003
metaresearch head score (Gemma)0.004
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 score0.731
Threshold uncertainty score0.542

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0580.028
Scholarly communication0.0100.003
Open science0.0020.007
Research integrity0.0020.007
Insufficient payload (model declined to judge)0.0040.001

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 head0.129
GPT teacher head0.399
Teacher spread0.270 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Educational Leadership Policy and PracticeSame topicIndigenous Health, Education, and RightsFrench-language works237,207