MétaCan
Menu
Back to cohort
Record W4387234202 · doi:10.1080/17508487.2023.2261472

Kîyokêwin (Visiting), leadership, and consenting to learn in public: indigenizing social sciences and humanities at the Royal Military College of Canada

2023· article· en· W4387234202 on OpenAlex
Danielle Lussier, James S. Denford

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCritical Studies in Education · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsIndigenousSociologyLawIndigenizationSupreme courtTreatyPolitical scienceMedia studies

Abstract

fetched live from OpenAlex

Espousing Indigenous Research Methods including Kîyokêwin/Visiting, beadwork as an embodied pedagogical and research practice, and storytelling, this article explores the authors’ experiences working in senior academic leadership positions to support indigenization at the Royal Military College of Canada. The authors consent to learn in public and discuss opportunities for role-modeling individual leadership in institutional contexts and for building small ‘safe enough’ spaces that generate energy and momentum that have the possibility of supporting broader-scale indigenization efforts. The authors understand the call to indigenize education as a non-negotiable imperative, and argue that it is an ethical responsibility for all who live in Canada to engage intentionally in concrete acts of reconciliation – in (re)building relationships with Indigenous Peoples.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.609
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.299
GPT teacher head0.461
Teacher spread0.162 · 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