Governmental Fiduciary Failure in Indigenous Environmental Health Justice: The Case of Pictou Landing First Nation
Bibliographic record
Abstract
From 1967 until 2020, [Community] has had 85 million litres of pulp and paper mill effluent dumped every day into an estuary that borders the community. Despite long-term concerns about cancer in the community, a federal government appointed Joint Environmental Health Monitoring Committee, mandated to oversee the health of the community, has never addressed [Community] concerns. In this study we accessed the 2013 Canadian Cancer Registry microfile data, and using the standard geographical classification code, accessed the cancer data for [Community], and provided comparable data for all Nova Scotia First Nations, as well as the county, provincial, and national population level data. We determined that digestive organ cancers, respiratory organ cancers, male genital organ cancers, and urinary tract cancers are higher in [Community] than at all comparable levels. Female breast and genital organ cancers are lowest in [Community] than at all other comparable levels. We note the limitation of this study as not being able to capture cancer data for off-reserve members at the time of diagnosis and the lapse in availability of up-to-date CCR data. This study demonstrates that cancer data can be compiled for First Nation communities using the standard geographic code, and although not a comprehensive count of all diagnoses for the registered members of [Community], it is the first study to provide data for those who lived in [Community] at the time of diagnosis. Moreover, it highlights the lack of capacity (or will) by Joint Environmental Health Monitoring Committee to uphold their fiduciary duty.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".