Bibliographic record
Abstract
The Northern Transportation Route is an ongoing remediation project conducted by Canadian Nuclear Laboratories that aims to clean up contaminations of water and land trails from transportation of pitchblende (a radioactive, uranium-rich mineral) on between the 1930s and 1960s in the Northwest Territories and Alberta, Canada. Parallel to this project, a Dene First Nation (Alberta, Canada) is leading its own community-based environmental monitoring to verify and assess past reports on contamination on its reserves. The portage trails along which pitchblende was transported hold a special place in the region, as crucial land trails connecting river routes. These traditional land routes were appropriated by companies and governments to support industrial and imperialist projects. Expansion of the fur trade, mining rush and the development of settler state transformed Indigenous land and water trails to support the transportation of extracted resources from the North to the South. In this paper, I examine the intersections and superpositions of Indigenous and imperial/ industrial transportation infrastructure. How did Indigenous infrastructures get enmeshed into industrial and imperial project and become toxic? How are these relations remembered and re-negotiated by inhabitants around those toxicities? What does it mean to remediate toxicities for local Dene communities? I consider portage trails, part of Indigenous infrastructures built on close relations between humans and non-humans, within the larger history of land appropriation and contamination by colonial projects with their toxic infrastructures of expansion (Armstrong et al., 2023). Following Cowen's division of infrastructure anchored in ontologies of supply (by state) and care (by Indigenous people) (in Pasternak et al., 2023), I examine the contaminated portage trails as coexisting, entangled infrastructures of care and toxicity, and the spilled uranium-ore as part of the expected failures and violence of infrastructure of transportation (Spice, 2018) affecting local Indigenous communities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.103 | 0.026 |
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".