Balance on Every Ledger: Kwakwaka’wakw Resource Values and Traditional Ecological Management
Bibliographic record
Abstract
This chapter illustrates the core environmental values of the Kwakwaka’wakw (Kwakiutl) people on the Pacific coast of Canada to explore how they manifest in the traditional management of coastal natural resources. The authors’ survey of environmental values is based on the authentic knowledge of Chief Adam Dick, a co-author of the chapter. The chapter argues that talking about Indigenous Knowledge without the broader context of environmental values can lead to serious scholarly misunderstandings and insists that long-term collaborations between academic researchers and specialized knowledge holders from Indigenous communities is necessary in order to represent Indigenous Knowledge accurately.\nThis chapter illustrates the core environmental values of the Kwakwaka’wakw people on the Pacific coast of Canada to explore how they manifest in the traditional management of coastal natural resources. These communities are world-renowned for their wealth: their abundance of natural resources and resource harvesting skill; their rich artistic and oral traditions; their ceremonials tradition that engages domains both tangible and intangible, seeking balance in the human and natural worlds. Outside of the Kwakwaka’wakw world, in the academic domain, much has been written regarding the connection between traditional environmental values and the resource management practices of Native American communities—a topic of perennial interest to both academic and popular audiences. In turn, these practices are guided by an underlying system of values and beliefs permeating all aspects of Kwakwaka’wakw culture.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".