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Record W2910320058 · doi:10.1080/13504622.2018.1485134

‘An atmosphere, an air, a life:’ Deleuze, elemental media, and more-than-human environmental subjectification and education

2019· article· en· W2910320058 on OpenAlexaffabout
Marcelina Piotrowski

Bibliographic record

VenueEnvironmental Education Research · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSubjectificationEnvironmental educationAtmosphere (unit)SociologyPedagogyEnvironmental scienceMeteorologyGeographyPhilosophy

Abstract

fetched live from OpenAlex

Subjectification in environmental movement education comprises an influx of more-than-human ‘others,’ including the classical elements: air, water, earth, fire. In this conceptual article, I consider what environmental movement education research, which includes inquiring into processes of political subjectification, might entail, when thinking with the elements. As part of this focus, I identify and propose thinking alongside an ‘elemental Deleuze,’ by attuning to how the elements thread through Deleuze’s many works. Thinking with the elements alongside the field of elemental media studies, I turn to a series of examples from research conducted on subjectification and education in anti-oil pipeline movements in British Columbia, Canada, to suggest that the elements are productive media of atmospheres and affects that generate political ‘life.’ My objectives in this article are to articulate implications and ways of engaging with more-than-human forms of subjectification in environmental movements, and the implications for environmental education research.

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)0.001
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 score0.218
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.025
GPT teacher head0.388
Teacher spread0.363 · 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

Citations10
Published2019
Admission routes2
Has abstractyes

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