Resource extraction and intersectoral research: Engaging accountable relations in the Environment Community Health Observatory Network
Bibliographic record
Abstract
The inaugural gathering of The Environment Community Health Observatory (ECHO) Network is a network of academic, non-profit, and health authority scholars and practitioners committed to understanding and responding to the cumulative impacts of resource extraction. The Network is embedded within multiple jurisdictions and institutional contexts, reflecting the Network’s efforts to work across sectors to address questions arising in communities and regions experiencing the overlapping influences of rurality, remoteness, and resource extraction. In this paper, we draw from entrance interviews and a group exercise with Network members to explore the complexity of accountability as an unfolding challenge for research that addresses resource extraction in Canada. We locate these findings within the current settler colonial context in which the Network is embroiled, arguing that a condition of settler colonialism is erasing not only Indigenous legal orders but also accountability mechanisms outside of state-based discourses. In making this argument, we understand settler colonialism as a failed yet persistent project. We contend that collectively engaging through situated and relational accountabilities beyond simply accounting for “accountability”, ECHO Network members—and others looking to social and environmental change—are critically challenged to approach settler-state apparatuses for transformative engagement beyond merely recognizing that accountability is relational.
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 | 0.036 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.048 | 0.053 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".