Becoming a Wild Researcher Through Goethean Science, Indigenous Philosophy and Creative Response
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.088 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".