Downstream River Dialogues: An Educational Journey Toward a Planetary-Scaled Ecological Imagination
Bibliographic record
Abstract
Purpose: This article aims to subvert the nature/culture and subject/object divides that structure the dominant Western educational research paradigm by stepping beyond an exclusively human conversation and activating our ecological imaginations in the face of intensifying anthropogenic climate change. Design/Approach/Methods: Informed by animist ecofeminist philosophies, the river dialogues emerged from a climate action research field trip to the Athabasca oil sand mines in Alberta, Canada. They perform a “more-than-human” mode of narrative engagement with “nature in the active voice.” Findings: Despite the epistemological separations of Western-style education, I conclude that we can still find ways to dialogue and learn with the nonhuman world and thereby to stimulate our ecological imaginations. Originality/Value: This article showcases innovative more-than-human narrative methods and offers a collaborative alternative to the human-centric conventions of educational research and pedagogy.
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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 | 0.011 | 0.006 |
| 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 | 0.025 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".