The Contribution of Aboriginal Traditional Ecological Knowledge to the Environmental Assessment Process for Canadian Pipelines
Bibliographic record
Abstract
Northern British Columbia (BC) and Alberta are sparsely populated forested lands under provincial jurisdiction (also known as Crown land) which are under intensive oil & gas exploration and pipeline development. Local Aboriginal people continue to implement traditional practices that maintain viable land and productive ecosystems by annual rotation of trap lines, hunting and gathering areas and similar activities. Aboriginal people can exert tremendous influence on pipeline projects through various means. Regulators and enlightened pipeline companies recognize the value of assessing traditional knowledge that has been collected over generations and passed down from the Elders to contribute to final routing, siting and project design identifying effects on environmental resources and traditional land and resource use and developing mitigation opportunities. Traditional knowledge includes experiential and secondary knowledge as well as accepted scientific research in the context of environmental assessments. Robust applications consider sources from all land users while being mindful of the intricacies inherent with Aboriginal engagement in order to gather substantive input for projects on Crown land. This paper explores the contribution of Aboriginal Traditional Ecological Knowledge (TEK) in the environmental assessment process on selected case studies involving recent natural gas pipeline projects in northern BC and Alberta from a balanced perspective. It also describes the evolution of a program developed by the author from its initial emphasis on Traditional Land Use (TLU) studies to the present day application of TLU studies, and TEK studies, focusing on lessons learned and regulatory and engagement challenges and successes.
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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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.024 | 0.011 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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".