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Record W4408991240 · doi:10.1017/aee.2025.9

Becoming a Wild Researcher Through Goethean Science, Indigenous Philosophy and Creative Response

2025· article· en· W4408991240 on OpenAlexaff
Lee Beavington

Bibliographic record

VenueAustralian Journal of Environmental Education · 2025
Typearticle
Languageen
FieldPsychology
TopicAnimal and Plant Science Education
Canadian institutionsKwantlen Polytechnic University
Fundersnot available
KeywordsIndigenousSociologyProject commissioningPublishingSocial sciencePedagogyEcologyArtLiteratureBiology

Abstract

fetched live from OpenAlex

Abstract Framed by biological and environmental education, this paper addresses eight questions posed in Wild Pedagogies: Touchstones for Re-Negotiating Education and the Environment in the Anthropocene. These questions ponder more-than-human methodologies, positionality of the natural world, embedded anthropocentricism and research implications for the natural world. Wild pedagogues aim to reclaim and reimagine an educational system toward intentional praxis less reliant on quantifiable learning outcomes, with a move toward active, ‘‘self-willed pedagogy’’ with an agential nature as co-teacher. This bold enterprise challenges dominant Western-colonial paradigms rooted in power and control over nature and learners. My responses explore Tim Ingold’s notion of a ‘‘modest, humble, and attentive’’ science, ecocentric place-based research, questions dissection and animal experimentation, and offers Goethean science and Indigenous philosophy as alternatives to rational-reductionist Newtonian science. Lab-based science is contrasted with natural history, and creative, contemplative practice are suggested as tools of the wild researcher. How can we transform science education through the lenses of deep ecology and philosophical posthumanism? This paper contributes to the ongoing dialogue of ecological and environmental education during the Anthropocene, especially in regard to the life sciences and the often-unquestioned use of nonhuman animals in science teaching and 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.557
Threshold uncertainty score0.861

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 studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.062
GPT teacher head0.393
Teacher spread0.330 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2025
Admission routes1
Has abstractyes

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