A cultural framework for Indigenous, Local, and Science knowledge systems in ecology and natural resource management
Bibliographic record
Abstract
Abstract The relationship between Indigenous, Local, and Science knowledge systems has been the subject of much debate over the past few decades, especially in ecology and natural resource management. In this monograph, we review available scholarship to develop a pragmatic framework for representation of knowledge systems in general, with specific emphasis on productive engagement between individuals from different communities and cultures. We distill operational definitions/explanations of fundamental concepts associated with data, information, knowledge, and wisdom. With these concepts clarified, we reconsider previous applications of sociocultural knowledge system thinking, focusing on system structure and function. Our analysis leads to convergence on a set of fundamental knowledge system processes and actor roles that have emerged repeatedly across many of the scholarly disciplines. We embed these key concepts within a general framework for operational characterization of sociocultural knowledge systems. In order to demonstrate existing and potential applications of the knowledge system framework, we present and discuss major trends in recent ecology and natural resource management literature. Finally, we propose that continued and collaborative development of this general framework can serve as a pragmatic tool for individuals from Indigenous, Local, and Science knowledge systems who wish to engage in reciprocal and meaningful dialogue with members of other knowledge systems, especially regarding the highly uncertain global future of ecology and natural resource management.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.049 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".