Biocultural Diversity and Indigenous Ways of Knowing: Reframing Human Ecology in the Arctic and Sub-Arctic
Bibliographic record
Abstract
At the dawn of the third millennium, dramatic challenges face human civilization everywhere. Relations between human beings and their environment are in peril, with mounting threats to both biological diversity of life on earth and cultural diversity of human communities. The peoples of the Circumpolar Arctic are at the forefront of these challenges and lead the way in seeking meaningful responses. In Biocultural Diversity and Indigenous Ways of Knowing, Karim-Aly Kassam positions the Arctic and sub-Arctic as a homeland rather than simply as a frontier for resource exploitation. Kassam aims to empirically and theoretically illustrate the synthesis between the cultural and the biological, using human ecology as a conceptual and analytical lens. Drawing on research carried out in partnership with indigenous northern communities, three case studies illustrate that subsistence hunting and gathering are not relics of an earlier era but rather remain essential to both cultural diversity and to human survival. This book deals with contemporary issues such as climate change, indigenous knowledge, and the impact of natural resource extraction. It is a narrative of community-based research, in the service of the communities for the benefit of the communities. It provides resource-based industry, policy makers, and students with an alternative way of engaging indigenous communities and transforming our perspective on conservation of ecological and cultural diversity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".