Meaningful consideration? A review of traditional knowledge in environmental decision making. Arctic 58(1
Bibliographic record
Abstract
ABSTRACT. In Canada’s Northwest Territories, governments, industrial corporations, and other organizations have tried many strategies to promote the meaningful consideration of traditional knowledge in environmental decision making, acknowledging that such consideration can foster more socially egalitarian and environmentally sustainable relationships between human societies and Nature. These initiatives have taken the form of both “top-down ” strategies (preparing environmental governance authorities to receive traditional knowledge) and “bottom-up ” strategies (fostering the capacity of aboriginal people to bring traditional knowledge to bear in environmental decision making). Unfortunately, most of these strategies have had only marginally beneficial effects, primarily because they failed to overcome certain significant barriers. These include communication barriers, arising from the different languages and styles of expression used by traditional knowledge holders; conceptual barriers, stemming from the organizations ’ difficulties in comprehending the values, practices, and context underlying traditional knowledge; and political barriers, resulting from an unwillingness to acknowledge traditional-knowledge messages that may conflict with the agendas of government or industry. Still other barriers emanate from the co-opting of traditional knowledge by non-aboriginal researchers and their institutions. These barriers help maintain a power imbalance between the practitioners of science and European-style environmental governance and the aboriginal people and their traditional knowledge. This imbalance fosters the rejection of traditional knowledge or its transformation and assimilation into Euro-Canadian ways of knowing and doing. Key words: traditional knowledge, environment, aboriginal, governance, power, Northwest Territories, policy, 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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.020 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".