Encountering the Great White Beast: Polar Bear Research as Ethical Space, Practice and Process of Engagement
Bibliographic record
Abstract
My dissertation explores the possibilities for ethical knowledge conciliation within community-based polar bear monitoring in Nunavut, Canada by putting feminist theorist and physics-philosopher Karen Barad’s agential realism into dialogue with Indigenous scholar Willie Ermine’s Ethical Space of Engagement (ESE). Situated within BearWatch: Monitoring Impacts of Arctic Climate Change using Polar Bears, Genomics and Traditional Ecological Knowledge—a Genome Canada-funded project that developed a non-invasive, community-based toolkit to monitor polar bears across Inuit Nunangat, this research asks: What does it mean, within the larger apparatus of community-based polar bear research, to practice knowledge conciliation guided by the principles of the ESE, rather than by data-driven needs? The dissertation draws from three years of fieldwork conducted with Inuit community members from Uqshuqtuuq (Gjoa Haven) and Salliq (Coral Harbour) in the Nunavut Settlement Area. Rather than viewing conciliation as a negotiation between epistemologies, it views knowledge production as intra-active processes where cross-cultural and disciplinary differences are materially and discursively enacted. Methodologically, my study employs a creative practice (auto-)ethnography, incorporating aesthetic actions like wayfaring, performance art, filmmaking, and collaging as tools for sensorial and performative engagement in-between more-than-human agencies including, but not limited to Inuit hunters, researchers, polar bears, qamutiit (sleds plural), sea ice, and seasonal changes. Written performatively, this dissertation unfolds in rounds and is accompanied by an interactive digital cartographic platform allowing readers to thread their own way through the research. Ultimately, this work reimagines ethical engagement as a shared wayfaring, where knowledge and ethics emerge through moving, making, and practicing research together.
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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 | 0.065 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.046 | 0.186 |
| Scholarly communication | 0.029 | 0.020 |
| Open science | 0.004 | 0.028 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".