Storied Watersheds: Indigenous Ecological Restoration, Nmé (Lake Sturgeon), and the Little River Ottawa
Bibliographic record
Abstract
This project on maamawijiwan, or watershed thinking, discusses the importance of nmé (Lake Sturgeon) for the Little River Ottawa. Through a close analysis of Anishinaabe stories, colonial laws, primary and secondary sources, and interviews, this dissertation shows that Anishinaabe philosophies support their own conservation strategies and the overall well-being of their morethan-human-relatives. Chapter One focuses on methods of Indigenous conservation and the values that guide relational research, rather than extractive research. Chapter Two foregrounds nmé and their vital role supporting plant health to show how the broader relationships between nmé and manoomin must be considered to move beyond colonial conservation practices. This chapter defines an ontology of ecological memory within Indigenous conservation practices. Chapter Three discusses archival evidence to understand the presences and absences of nmé in colonial history. Chapter Four focuses on interviews conducted during 2021-2022 with Little River Ottawa Tribal members and Elders. These interviews and my analysis argue for centering Indigenous narrations and perspectives of climate change. In my conclusion, I bring together reflections on the current climate crisis and look to ways that maamawijiwan thinking and Indigenous conservation allow for a collective and critical knowing of our worlds around us.
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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 | 0.000 | 0.001 |
| 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 | 0.009 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".