Addressing the Environmental, Community, and Health Impacts of Resource Development: Challenges across Scales, Sectors, and Sites
Bibliographic record
Abstract
Work that addresses the cumulative impacts of resource extraction on environment, community, and health is necessarily large in scope. This paper presents experiences from initiating research at this intersection and explores implications for the ambitious, integrative agenda of planetary health. The purpose is to outline origins, design features, and preliminary insights from our intersectoral and international project, based in Canada and titled the “Environment, Community, Health Observatory” (ECHO) Network. With a clear emphasis on rural, remote, and Indigenous communities, environments, and health, the ECHO Network is designed to answer the question: How can an Environment, Community, Health Observatory Network support the integrative tools and processes required to improve understanding and response to the cumulative health impacts of resource development? The Network is informed by four regional cases across Canada where we employ a framework and an approach grounded in observation, “taking notice for action”, and collective learning. Sharing insights from the foundational phase of this five-year project, we reflect on the hidden and obvious challenges of working across scales, sectors, and sites, and the overlap of generative and uncomfortable entanglements associated with health and resource development. Yet, although intersectoral work addressing the cumulative impacts of resource extraction presents uncertainty and unresolved tensions, ultimately we argue that it is worth staying with the trouble.
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 | 0.035 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.027 | 0.040 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.005 | 0.032 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".