Traditional Ecological Knowledge in the Canadian Impact Assessment Process
Bibliographic record
Abstract
This study explores traditional ecological knowledge through a qualitative and semi-quantitative inquiry of seven Indigenous Elders and Knowledge Keepers. The aim of this project was to describe the epistemology of traditional ecological knowledge in a way that is relatable to regulators and western scientists. The broad objectives of this project were to conduct research and investigate through an Indigenous research methodology and determine how western educated scientists and engineers can be further informed of the importance and significance of TEK in the context of environmental impact assessments. The Impact Assessment Agency of Canada (IAAC) regulates the environmental approval of designated projects through the Impact Assessment Act. Proponents of projects designated under the Act are legally required to use any traditional ecological knowledge (TEK) provided with respect to the project, along with scientific information in determining whether the project should proceed. Standard western science-based research tools for collecting and analyzing qualitative information involving people was used and supplemented by including quantitative analyses where appropriate. The methodology used in this study is based on an interpretive, deductive/inductive, and narrative inquiry-based approaches that are respectful of Indigenous peoples, their protocols, and their knowledge systems. Semi-structured interviews were used for interviewing Indigenous Elders and Knowledge Keepers. The results indicate the epistemology of traditional ecological knowledge is a life-long process, that entails experiential, tangible, intangible, observational, spiritual and generational influences. The findings also show that a strong set of moral principles and ethics are deeply embedded within the Indigenous knowledge system that guide an Elder’s or Knowledge Keeper’s behavior within the paradigm of traditional ecological knowledge. The findings of this study also reveal several benefits of including traditional ecological knowledge in the impact assessment process. Traditional ecological knowledge is much broader in scope than western science and therefore it can provide a greater understanding of the past and present baseline conditions of an area proposed for development. Furthermore, including traditional ecological in the impact assessment process could help to reduce project delays, thereby reducing costs to project proponents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.029 | 0.027 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.011 |
| 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".