Visions of Consent Nunavummiut Against the Exploitation of “Resource Frontiers”
Bibliographic record
Abstract
\n \n \nDespite a long history of colonial, military, and extractive industry imposition on the land, waters, and people of Inuit Nunangat, resistance to such efforts is thriving. Through highlighting the work of The Place Names Program and Arnait Video Productions, I show how Nunavummiut (the people living in Nunavut) employ visual media to publicly wage their place-based knowledge as a mode of creative intervention against military and extractive forces, and the ways in which such forces have permeated Inuit bodies, lands, and waters. So successful are these visual acts of resistance that they compel southerners to reevaluate their approaches to northern development so drastically that projects are abandoned or no longer seen as viable. In putting these strategies into practice, Inuit engage with state-sanctioned systems of law and governance, but ultimately reshape these structures to better suit their own needs and the needs of the Arctic land and sea. The maps produced by the Place Names Program and films produced by Arnait Video Productions resist visions of the Arctic as a wasteland and of Inuit bodies as pollutable, instead putting forward visions of consent and reciprocity. Ultimately, I argue that seeing the Arctic in ways that challenge military and extractive representations and center Inuit epistemologies and voices, plays a significant role in halting the continued molecular and chemical colonization of Inuit lands and bodies. In other words, visual media is a tool for resisting unwanted extractive and military bodily intimacies, and insisting on consent before entry of these toxic presences.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.020 | 0.027 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| 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".