Storylines of research on resource extraction and health in Canada: A modified metanarrative synthesis
Bibliographic record
Abstract
Patterns of research on resource extraction's health effects display problematic gaps and underlying assumptions, indicating a need to situate health knowledge production in the context of disciplinary, corporate and neocolonial influences and structures. This paper reports on a modified metanarrative synthesis of 'storylines' of research on resource extraction and health in the Canadian context. Peer-reviewed articles on mining or petroleum extraction and health published between 2000 and 2018 and dealing with Canadian populations or policies (n = 87) were identified through a systematic literature search. Coding identified main disciplinary traditions, methodologies and approaches for judging high-quality research. Underlying assumptions were analyzed in terms of models of health and well-being; resource extraction's political economic drivers; and representations of Indigenous peoples, territories and concerns. Findings included a preponderance of occupational and environmental health studies; frequent presentation of resource extraction without political economic antecedents, and as a major contributor to Canadian society; sustainable development aspirations to mitigate health impacts through voluntary private-sector governance activities; representations of Indigenous peoples and concerns ranging from complete absence to engagement with legacies of historical trauma and environmental dispossession; and indictment of corporate (especially asbestos industry) and government malfeasance in a subset of studies. Canada's world-leading mining sector, petroleum reserves and population health traditions, coupled with colonial legacies in both domestic and overseas mining and petroleum development, make these insights relevant to broader efforts for health equity in relation to resource extraction. They suggest a need for strengthened application of the precautionary principle in relation to resource extraction; nuanced attention to corporate influences on the production of health science; more profound challenges to dominant economic development models; and extension of well-intentioned efforts of researchers and policymakers working within flawed institutions.
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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 | 0.122 | 0.260 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.050 | 0.078 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".