The Problem of Scale in Indigenous Knowledge: a Perspective from Northern Australia
Bibliographic record
Abstract
Over the last decade, indigenous knowledge has been widely touted by researchers and natural resource managers as a valuable contributor to natural resource management and biodiversity conservation.In Australia, the concept of indigenous knowledge has gained such rapid currency that it has tended toward an essentialized and universal truth rather than remaining a diverse range of highly localized and contested knowledge.In this paper, I undertake a critical analysis of some of the current issues around the interpretation and application of indigenous knowledge and its relationship with natural resource management in northern Australia.Through a focus on how indigenous knowledge operates at a range of scales, I argue that indigenous knowledge is not adapted to the scales and kinds of disturbances that contemporary society is exerting on natural systems.Rather than being realistic about the limitations of indigenous knowledge, I argue that nonindigenous interpretations of indigenous knowledge have propelled us toward reified meanings, abstracted concepts, and an information-based taxonomy of place.The result can be the diminishing and ossifying of a dynamic living practice and the failure to recognize expressions of indigeneity in contemporary forms.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.044 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".