Practices, events, and effects: Improving causal analysis with the geographic information from cultural mapping in Canada
Bibliographic record
Abstract
Most questions that environmental impact assessments (EIAs) aim to answer are not statistical but seek to understand the interactions between proposed projects and valued components representing local environments. Assessing causality provides critical insights into the potential impacts of project proposals, informing decisionmaking processes aimed at sustainable development. However, despite wellestablished causal analysis techniques in EIAs, these procedures are rarely adapted to incorporate the unique traditional ecological knowledge (TEK) and circumstances of Indigenous peoples. This paper modifies the stepped matrix by integrating TEK with geographic qualities from cultural mapping studies to enhance causal analyses involving events of cultural practices and project proposals. The modified procedures employ both theoretical and empirical approaches, accounting for the historical and contemporary contexts of Indigenous peoples, the spatiotemporal traits of their cultural practices, and the challenges of cultural mapping. The results demonstrate that the TEK-modified stepped matrix improves causal analysis by identifying sub-patterns, differences in geographic scales, interdependencies of cultural events, and causal networks, while refining the understandings of potential direct and indirect project-related effects, cumulative effects , and the efficacy of mitigation measures .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".