A Canadian Selvage: Weaving Artistic Research into Resource Politics
Bibliographic record
Abstract
This exploratory article addresses our experiences as artist-researchers engaged with “Trading Routes: Grease Trails, Oil Futures,” a research-creation project supported by the Social Sciences and Humanities Research Council of Canada. “Trading Routes” focuses on the intersecting geographies of Indigenous fish grease trails and the proposed Alberta-British Columbia oil pipeline. These converging routes are shedding light on the present entanglement between Indigenous and non-Indigenous cultural heritage, ecological perspectives, and resource extraction. Through artistic scholarship, material production, historical and cultural understanding, we seek to better account for the ways in which an environmental social justice perspective can be crafted into arts-based research. We write from a point of reflection, where we assess, evaluate, disentangle, and unclad some of the learning that has come to us through the research-creation and presentation of contemporary weaving. We suggest that arts-based research can offer a methodology of learning and thinking rooted in a perspective of informing, informality, or thinking about artworks in form, an extension of a/r/tographic praxis that is grounded in an analysis of materiality and aesthetics.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.061 | 0.063 |
| Scholarly communication | 0.020 | 0.006 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".