A biologist's perspective on amalgamating traditional environmental knowledge and resource management
Bibliographic record
Abstract
Recent transitions in resource management and recognition of the role of First Nations in resource management have heightened the need for conciliation of these two different views of the world and the place of people in it (world view). Efforts to amalgamate these diverse perspectives in resource management are impeded by a legacy of cultural imperialism and difficulties in understanding and accommodating differences in world views, including the place of resource management in society, the meanings and implications of incongruent language, the management of people and resources, and the characteristics of information and knowledge. We examine the consequences of each of these to the establishment of a unified management system that emphasizes points of commonality and is based on respect and communication.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.105 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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".