Apoqnmatulti’k: Turning the tide for collaborative research
Bibliographic record
Abstract
A collaborative and holistic approach is essential to achieving a healthy and resilient aquatic ecosystem. Apoqnmatulti’k (Mi’kmaw for “we help each other”) is a partnership that involves the Unama’ki Institute of Natural Resources, the Confederacy of Mainland Mi’kmaq, commercial fisher Darren Porter, the Ocean Tracking Network, Acadia University, Dalhousie University, and Fisheries and Oceans Canada-Science. Apoqnmatulti’k is founded on the shared participation of Mi’kmaw, local, and Western scientific knowledge holders, aiming to better understand valued aquatic species in Pitu’pa’q (Bras d’Or Lake) and Pekwitapa’qek (Minas Basin). Guided by the principle of Etuaptmumk (Two-Eyed Seeing), Apoqnmatulti’k serves as a model for how the incorporation of diverse perspectives can enhance knowledge, ensure transparency and accessibility of information, and transform fisheries management and conservation. This paper focuses on the challenges, lessons learned, and achievements derived from collaboration and the development of a strong partnership.
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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 | 0.056 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.020 |
| Scholarly communication | 0.021 | 0.019 |
| Open science | 0.003 | 0.034 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".