Caribou co-management in northern Canada: fostering multiple ways of knowing
Bibliographic record
Abstract
Introduction The links between social and ecological systems are represented by diverse ways of looking at human–environment relations. The continuing exchange between different ways of knowing may be crucial to integrative thought about social–ecological linkages. For many indigenous societies, the separation of social and ecological systems does not make sense. A ‘human–environment’ divide is especially absent from many arctic and subarctic cultures. How does this fundamental ideological difference play out in resource management systems that incorporate stakeholders both from ‘the West’ (Euro-American) and from indigenous cultures for whom a human–environment or social–ecological divide is a relatively new and foreign concept? This chapter looks at the differences that exist in the perceptions of indigenous caribou-using communities, caribou managers, and scientists in co-management processes in arctic and subarctic North America. It is contended that these differences represent potentials to expand how we think about human– Rangifer (caribou) systems as much as they represent obstacles to caribou research, monitoring, and management decision-making. The process of negotiating cross-cultural differences in the co-management of caribou herds indicates the potential for the growth of alternative resource management systems capable of accommodating varied ways of knowing and learning. The question of how humans learn to respect other ways of knowing is represented here as an examination of humility, a respect for diverse realities. There are multiple epistemologies outlining ethical positions of human–environment relations and human perceptions of nature (Folke, Berkes, and Colding, 1998).
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.028 | 0.016 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.009 |
| 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".