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Record W7051995987

Places of Knowing, Places of Learning: Indigenous Place-Based Education in Canada

2014· other· en· W7051995987 on OpenAlexaboutno aff

Bibliographic record

VenueLund University Publications Student Papers (Lund University) · 2014
Typeother
Languageen
FieldEngineering
TopicPlasma Diagnostics and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousMainstreamTransdisciplinarityIndigenous educationSituatedEnvironmental educationTraditional knowledgeCurriculum
DOInot available

Abstract

fetched live from OpenAlex

This thesis reviews the literature on indigenous place-based environmental education in Canada. The concept of place is considered a starting point to localize, decolonize and integrate indigenous and non-indigenous knowledges (the culturally-situated subjective and intersubjective ways of knowing and meaning-making) in mainstream environmental education. Following a discussion of how a critical pedagogy of place can be situated in indigenous contexts, this thesis explores how indigenous and non-indigenous peoples and their knowledges can contribute to a place-based environmental education. While mainstream environmental education is conventionally considered the domain of Western sciences, knowledges of all cultural groups are needed to address the environmental challenges of the 21st century and enrich sustainability education. The inclusion of indigenous and other knowledges in mainstream curricula can foster intercultural understanding between indigenous and non-indigenous peoples. This can help to heal the relationship between indigenous and non-indigenous peoples in Canada after centuries of colonialism, assimilation, and discrimination against indigenous peoples. Transdisciplinarity and social learning theory can provide epistemological and methodological frameworks for the integration of indigenous and other knowledges in mainstream environmental education for an inclusive, place-based education.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.808
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.003
GPT teacher head0.166
Teacher spread0.163 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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
Published2014
Admission routes1
Has abstractyes

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