A Two-Eyed Seeing approach to describe Gumegwsis (Cyclopterus lumpus) ecology and fisheries interactions in the inner Mawipoqtapei (Chaleur Bay), Canada
Bibliographic record
Abstract
Abstract The integration of diverse knowledge systems, encompassing Indigenous, local, and Western perspectives, is gaining traction in Canadian scientific research for coastal areas and fisheries. Despite proven successes, skepticism persists among scientists and decision-makers, leading to ineffective recovery measures for endangered aquatic species. Responding to concerns from Mi’gmaw fishers in Ugpi’ganjiq, the Gespe’gewa’gi Institute of Natural Understanding (GINU) initiated a collaborative project focused on the threatened Gumegwsis (Common lumpfish) in Chaleur Bay, Eastern Canada. Employing a Two-eyed seeing approach, the study combined interviews, mapping, and temperature monitoring, uncovering Gumegwsis life history, its significance to local fishers, behavioral changes, and critical spawning and nursery habitats. In contrast to prior assessments, which dismissed ceremonial and Aboriginal Traditional Knowledge (ATK) uses, our study highlighted the unique insights of Mi’gmaw fishers, emphasizing the importance of embracing diverse knowledge for species ecology and habitat understanding. This underscores the need for collaborative species recovery strategies, advocating for the co-creation of solutions and fostering cooperation in fisheries research.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".