If only they had accessed the data: Governmental failure to monitor pulp mill impacts on human health in Pictou Landing First Nation
Bibliographic record
Abstract
For over fifty years, Pictou Landing First Nation (PLFN), a small Mi'kmaw community on the northern shore of mainland Nova Scotia, Canada, has been told by a Joint Environmental Health Monitoring Committee (JEHMC) mandated to oversee the health of the community that their health has not been impacted by exposure to 85 million litres of pulp mill effluent dumped every day into what was once a culturally significant body of water bordering their community. Yet, based on lived experience, the community knows otherwise, and despite countless dollars spent on government and industry-sponsored research, their concerns have not gone away. Using biopolitical theory, we explore why JEHMC never fully implemented its mandate. We will use a Mi'kmaw environmental 'theoretical' framework to demonstrate that indicators of a relational epistemology and ontology that have been consistently and persistently overlooked in Indigenous environmental health research demands that Indigenous connections to the air, land and water must be taken into consideration to get a full understanding of environmental health impacts. Guided by the principle of Etuaptmumk (Two-Eyed Seeing), which brings together the strengths of both western and Indigenous knowledge, and employing a community-based participatory research approach, we use data that could have been accessed by the JEHMC that might have signaled that human health studies were warranted. Further, we developed an environmental health survey that more appropriately assesses the impacts on the community. Finally, we will discuss how an Indigenous-developed framework can adequately assess the impacts of land displacement and environmental dispossession on the health of Indigenous communities and illustrate how our framework can serve as a guide to others when exploring Indigenous environmental health more broadly.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".