SEE. I AM ONE. BALANCING POWER IN GENOMICS RESEARCH OF CONSTRUCTED WETLANDS TO CLEAN OIL SANDS TAILINGS WATER
Bibliographic record
Abstract
Within a large-scale Genome Canada program examining genomic enhancement of constructed treatment wetland systems (CTWS) to remediate oil sands processed water (OSPW), I was positioned to study the researchers, research, and community perspectives, to contribute to a gap on how these collaborations can increase their effectiveness in using multiple ways of knowing. My work proposed to even-out power imbalances in several ways; studying the findings of affected communities1 and researchers who weave knowledge toward CTWS design and observing the project scientists, their interactions, and intellectual exchanges in their evolving network as a subject for my artwork. In this project, I used research creation in place of conventional research translation. Research creation is a powerful vehicle to assist in conveying new knowledge through the lens of art. My work commented on the weak and strong collaborations, or, in some places, lack thereof, of Indigenous, corporate, government, and non-profit representatives through research creation while supporting regenerative sustainability, by balancing mental and physical analysis (conventional research) with moral, emotional, and intercultural (introspection + building relationality + creative action) aspects of the project to better represent complete ecosystems. Finally, my work also acts as an instrument for education, a conduit of information and inquiry to reach local and wide audiences, and communities of practice in a more accessible way than the conventional scientific paper.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.015 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".