Two-Eyed Seeing: Developing perspective and wisdom on sea lamprey in the Laurentian Great Lakes
Bibliographic record
Abstract
Bridging knowledge systems is a potential means of equitably and collaboratively working towards improved conservation and management of aquatic ecosystems, such as the management of invasive species. Etuaptmumk (Two-Eyed Seeing) is a Mi’kmaw framework that encourages the bridging of Indigenous and Western knowledge systems to work together in parallel on a shared issue or problem. Invasive species pose a significant threat to aquatic ecosystems, and they are disrupting fisheries and entire lake ecosystems within the Laurentian Great Lakes. Sea lamprey (Petromyzon marinus) are one example of an invasive species in these Great Lakes with an established control program. Sea lamprey management faces many challenges including possible declining social acceptance of control programs, especially amongst Indigenous communities in the region. Such challenges illustrate the need for sea lamprey management to be resilient, sustainable, and reflective of the knowledges and needs of the people across the Laurentian Great Lakes. We argue that applying the guidance offered by Two-Eyed Seeing to sea lamprey management could help uphold Indigenous rights and knowledges in resource management and be an important step towards remedying the historical and contemporary exclusion of Indigenous Nations in decision-making concerning the Laurentian Great Lakes fisheries. Specifically, we explore why Two-Eyed Seeing should be applied and how it can guide non-Indigenous government agencies and fisheries organizations across the Laurentian Great Lakes region to expand and deepen their partnerships with Indigenous Nations for more equitable decision-making while enhancing the collective state of knowledge in the interests of re-envisioning and enhancing sea lamprey control.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.020 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".