Deepening the debate over ‘sustainable science’: Indigenous perspectives as a guide on the journey
Bibliographic record
Abstract
Abstract This article engages with the concept of sustainable science as articulated by those eager to address and correct environmentally destructive tendencies in western scientific theory and practice. We first reflect on the widespread resistance among western scientists to accord the designation of ‘science’ to other cultural enterprises of inquiry. Focusing on the example of Native American approaches to nature and knowledge, we caution that this pervasive sense of superiority has blocked recognition of reasonable paths to a new science even amongst those eager to incorporate elements of Indigenous thinking into their worldviews. Finally, we argue that the explorations of the natural world as found in Indigenous science can be seen to represent an alternative mode of rigorous, systematic inquiry – a ‘full‐spectrum’ approach – demonstrating the practical potential for truly sustainable science. Copyright © 2009 John Wiley & Sons, Ltd and ERP Environment.
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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 | 0.028 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.072 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 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".