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Record W2286823667

Indigenous Knowledges and Western Knowledges in Environmental Education: Acknowledging the Tensions for the Benefits of a “Two-Worlds” Approach

2012· article· en· W2286823667 on OpenAlexaffvenue
Julie Kapyrka, Mark S. Dockstator

Bibliographic record

VenueCanadian journal of environmental education · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsTrent University
Fundersnot available
KeywordsIndigenousSociologyEnvironmental educationEnvironmental ethicsTraditional knowledgeEpistemologySocial sciencePedagogyEcology
DOInot available

Abstract

fetched live from OpenAlex

Indigenous worldviews and Western worldviews stand in stark contrast to each other in many ways, including their perspectives regarding the Earth and her resources. Typically the differences between these two philosophies of life are highlighted and placed into an antagonistic relationship that seems irreconcilable. This paper upholds that within this tension there is a great opportunity for learning and for mutual understanding. We argue for using a “two-worlds” approach that engages both Indigenous knowledges and Western knowledges within environmental education. A “two-worlds” approach has the capacity to enlighten both educators and students and promote relationship-building between Indigenous and non-Indigenous peoples and their respective cultural understandings. Two worldviews can be drawn upon to create collaborative models and solutions to address our collective environmental challenges.

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)0.010
metaresearch head score (Gemma)0.006
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.067
Threshold uncertainty score0.133

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0160.079
Scholarly communication0.0180.014
Open science0.0020.012
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0030.000

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 head0.013
GPT teacher head0.251
Teacher spread0.238 · 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

Citations44
Published2012
Admission routes2
Has abstractyes

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