Growing (with) Muskeg: Oil Sands Reclamation and Healing in Northern Alberta
Bibliographic record
Abstract
Scientists working for oil companies in the Athabasca region are developing methods by which to reclaim muskeg (boreal peatlands) on land disturbed by oil sands extraction. The Alberta government requires companies to reclaim disturbed land by achieving equivalent capability of the landscape to support an end land use. Indigenous community members instead define reclamation as establishing not only quantifiable ecological functions, but also relationships to their traditional territories. Tensions emerge as Indigenous concerns are often subsumed within bureaucratic discourses that favour scientific classification and quantification of land uses in reclaimed areas. Divergent responses to muskeg in reclamation activities are informed in part by these competing emphases on quantifiable landscapes as opposed to those that are relational and growing. This article traces this multiplicity through the examination of government and scientific literature and ethnographic fieldwork with Indigenous communities in northern Alberta. Muskeg is used as an analytical tool to explore competing conceptions of land reclamation. Mistranslation of polysemantic terms like muskeg occur on an ontological level, and settler colonial relations and power imbalances between competing languages and knowledge systems proliferate in reclamation activities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.007 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".