Indigenous and decolonial approaches to environmental learning
Bibliographic record
Abstract
The 2025 Special Issue of AlterNative: An International Journal of Indigenous Peoples —“ Living a Life that Feels Just Right”: Indigenous and Decolonial Approaches to Environmental (Re)learning for Planetary Care in a Time of Climate Uncertainty , envisioned by guest editors Elizabeth Sumida Huaman and Sharon Stein, goes beyond merely describing ecological precarity in different places around the world. It brings together diverse learning interventions, many of which unfold outside of formal classrooms, that explore how people are talking about, researching, and teaching approaches to environmental engagement that seek to mend the separation between humans and the rest of nature. The Guest Editors share their motivations for assembling the special issue, observe major tensions in decolonial climate education work, and provide an overview of the contributions. We present key questions we are asking ourselves and key challenges we are observing, and we invite readers to consider these in their own learning contexts.
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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 | 0.005 | 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 | 0.007 | 0.018 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".