Rebuilding fish-human relationships by quantifying combined toxicity and evaluating policy related to legacy contamination
Bibliographic record
Abstract
The central theme of this dissertation is relationships – building relationships as research partnerships, disrupting relationships through chemical contamination, and upholding existing relationships (i.e., responsibilities) to address industrial legacies. In partnership with the Keweenaw Bay Indian Community Lake Superior Band of Chippewa Indians (KBIC), this dissertation focuses on rebuilding fish-human relationships within the context of chemical contamination. By quantifying combined toxicity and evaluating the efficacy of cleaning up contamination, conclusions from this work help empower people to maintain practices and knowledges related to fish. In chapter 1, I positioned myself, a white, American settler scholar, within the context of Indigenous research grounded in Anishinaabe philosophies. My research is predicated on knowledge being a collection of practices that builds and maintains relationships with people and the environment. Being an indigenist researcher means being accountable to those relationships. In chapter 2, I co-created a research guidance document with KBIC to provide holistic guidance and specify support that enriches their efforts to protect and restore land and life. Our guidance uses the Medicine Wheel to illustrate an interconnected system of partnership teachings that include systems of mutual expectations and responsibilities. The guidance aims for balance between and among four seasons of research: relationship building, planning and prioritization, knowledge exchange, and synthesis and application. In chapter 3, I used a national database of fish tissue contaminant concentrations to evaluate frameworks for quantifying toxicity, spatial distributions of the components of toxicity, and variations in relative importance of chemicals in different fish types. Based on the results, I argue for using the most sensitive endpoint for components of a chemical mixture rather than the current framework that expects a shared toxic pathway. Research results show that the former is more protective and therefore represents a more appropriate strategy for protecting human health and the environment. In chapter 4, I compared PCB trends in the Great Lakes basin to evaluate the efficacy of Canada’s 2008 PCB reduction policy. My results show that local reductions of PCB stocks significantly reduced atmospheric PCB concentrations, but a comparable response was not seen in fish tissue. I suggest that fish tissue, as the primary exposure pathway, should be the medium monitored to evaluate policy efficacy.
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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 | 0.023 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".