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Record W1570925767

Nature, Empire and Paradox in Environmental Education

2010· article· en· W1570925767 on OpenAlexvenueno aff
David A. Greenwood

Bibliographic record

VenueCanadian journal of environmental education · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous and Place-Based Education
Canadian institutionsnot available
Fundersnot available
KeywordsEmpireEnvironmentalismEnvironmental educationEnvironmental ethicsSociologyOpposition (politics)GeopoliticsGlobalizationColonialismContext (archaeology)EpistemologySocial sciencePolitical scienceLawHistoryPolitics
DOInot available

Abstract

fetched live from OpenAlex

As part of the 2009 North American Association of Environmental Education Research Symposium, this article addresses the cultural and theoretical frameworks that we bring to environmental education, the web of ideas and experiences that define the scope and purpose of the work in its geopolitical context. Originally delivered as a keynote address at the symposium, the paper highlights two necessarily related conversations within environmental education: the first concerns the problem of empire, including its roots in imperialism and colonialism, as well contemporary problems of globalization; the second concerns the problem of nature, including the need to develop intimate connections with the non-human on a planet that everywhere bares the mark of human alteration. Nature and empire are two poles on a continuum that shape the cultural and ecological contexts of life and learning. The author argues for the need to hold empire and nature not in opposition, but in paradox. Holding the tension of paradox complicates simplistic binaries, and can contribute to a stance that appreciates the relationships between seeming polarities in the intersectional work of social and ecological change.

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)0.008
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.975
Threshold uncertainty score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0110.058
Scholarly communication0.0110.012
Open science0.0010.008
Research integrity0.0020.005
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 head0.003
GPT teacher head0.241
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 designNot applicable
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

Citations12
Published2010
Admission routes1
Has abstractyes

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