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Record W2265844009 · doi:10.1080/13504622.2015.1121378

Shifting relations with the more-than-human: six threshold concepts for transformative sustainability learning

2016· article· en· W2265844009 on OpenAlexafffund
Matthew J. Barrett, Matthew Harmin, Bryan Maracle, Molly Patterson, Christina Thomson, Michelle Flowers, Kirk Bors

Bibliographic record

VenueEnvironmental Education Research · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsUniversity of Saskatchewan
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsTransformative learningEpistemologySociologySustainabilityEmbodied cognitionIntuitionProcess (computing)Action researchAction (physics)Environmental educationEngineering ethicsPedagogyComputer scienceEcology

Abstract

fetched live from OpenAlex

Using the iterative process of action research, we identify six portals of understanding, called threshold concepts, which can be used as curricular guideposts to disrupt the socially constituted separation, and hierarchy, between humans and the more-than-human. The threshold concepts identified in this study provide focal points for a curriculum in transformative sustainability learning which (1) acknowledges non-human agency; and (2) recognizes that the capacity to work with multiple ways of knowing is required to effectively engage in the process of sustainability knowledge creation. These concepts are: there are different ways of knowing; we can communicate with non-human nature and non-human nature can communicate with us; knowing is relational; transrational intuition and embodied knowing are valuable and valid ways of knowing; worldview is the lens through which we view reality; and the power of dominant beliefs (represented in discourse) supports and/or undermines particular ways of knowing and being as in/valid.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.165
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.003
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.018
GPT teacher head0.369
Teacher spread0.351 · 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 designObservational
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

Citations73
Published2016
Admission routes2
Has abstractyes

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