Chapter 20: Science and Indigenous Knowledge as the Evidentiary Basis for Impact Assessment
Bibliographic record
Abstract
In this chapter we consider the Impact Assessment Act’s approach to science and Indigenous knowledge. We begin by setting out the roles that science and Indigenous knowledge play in establishing the evidentiary basis for impact assessment and decision-making. We also consider the unsatisfactory manner in which science and Indigenous knowledge have been applied over the past four decades of Canadian impact assessment law and practice, and some of the factors that have been identified as contributing to this state of affairs. Having set the stage in this way, we then consider the specific provisions contained in the IAA with respect to science and Indigenous knowledge. Regarding science, much was said by the Liberal government during the period that led to the IAA’s development about the need for increased transparency and scientific rigour, as it was by the government-appointed Expert Panel on Federal Environmental Assessment Processes. While the IAA does reflect important gains on this front, including a new duty of scientific integrity, these alone are unlikely to yield the transformative change envisioned by the Expert Panel and anticipated by many observers as key to improving confidence in decision making regarding development projects. The improvements with respect to Indigenous knowledge are more significant but similarly do not address all of the impediments to its meaningful use in impact assessment. We end by considering some of the steps that must be taken to improve the application of science and Indigenous knowledge under the IAA to best ensure a strong evidentiary basis for decision making.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.028 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".