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Turtle Island (North America) Indigenous Higher Education Institutions and Environmental Sustainability Education

2021· article· en· W3188824508 on OpenAlexaffabout
Kelsey Leonard

Bibliographic record

VenueJournal of Comparative & International Higher Education · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsIndigenousSustainabilityContext (archaeology)Higher educationIndigenous educationPolitical scienceEconomic growthEnvironmental educationGeographyPublic administrationEcologyEconomicsArchaeology

Abstract

fetched live from OpenAlex

This article explores the environmental and sustainability programs of Indigenous Higher Education Institutions (IHEIs) in North America. There are 38 Tribal Colleges and Universities in the United States and 26 Indigenous post-secondary institutions in Canada. Deploying a critical discourse analysis, the study examines IHEI websites to document Indigenous environmental sustainability education (ESE) program offerings. The comparative analysis of IHEI programming in each national context finds that 41 out of 62 IHEIs in Canada and the United States have Indigenous ESE programs. Findings also indicate that ESE programs are more prevalent among IHEIs in the United States than in Canada. Moreover, IHEIs in the United States also offered greater diversity of program types, from certificates to graduate studies. The findings highlight the importance of IHEI environmental and sustainability education program design for centering Indigenous Knowledge in higher education through Indigenous-controlled institutions.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.121
Threshold uncertainty score0.240

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.015
GPT teacher head0.321
Teacher spread0.306 · 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

Citations3
Published2021
Admission routes2
Has abstractyes

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