Health determinants in Canadian northern environmental impact assessment
Bibliographic record
Abstract
The need to address the human health implications of northern development is well founded, and the role of health determinants in environmental impact assessment is increasingly recognised; however, there is limited understanding of the nature of health determinants and current practices in northern project assessment and decision making. This paper reports on a study of the nature and use of health determinants in Canadian northern environmental impact assessment, and discusses the key challenges to, and opportunities for, improved practice. Four themes emerged from this study. First, the consideration of health is limited to physical environments and the physical determinants of health, with limited attention to broader social and cultural health determinants. Second, when health is considered in northern project impact assessments such considerations rarely carry forward to post-project approval monitoring of health determinants and evaluation of health impact management programmes. Third, while there is general consensus that health determinants should be an integral part of northern impact assessment, there exist different expectations of the role of health determinants in project evaluation and decision making due in large part to different understandings and interpretations of health. Finally, a broader conceptualisation of health and health determinants in northern environmental impact assessment is required; one that takes into consideration northern cultures and knowledge systems, and is adaptive to local context, geography and life cycles.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| 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".