St. Lawrence River Strategy: Connecting community for a beautiful and healthy Kaniatarowanenneh
Bibliographic record
Abstract
The challenges of responding to complex environmental issues and widespread environmental injustice have led to calls for greater collaborative engagement and participation in environmental management and knowledge production. The need for innovative approaches to collaboration and participation is particularly relevant in the context of transboundary systems, like that of the Kaniatarowanenneh (St. Lawrence River), which spans the borders dividing Canada, the United States, Indigenous nations, and three states/provinces. Like many large, transboundary waterbodies, the St. Lawrence River has been impacted by a range of environmental stressors, including large-scale hydrological transformations, pollution, invasive species, and habitat degradation from land and water use. In response, various actors, including Indigenous and non-Indigenous government agencies, academic researchers, community groups, and non-profit organizations, have been involved in monitoring and restoration projects along the river. For over a decade, however, local and regional groups and organizations have also identified a need for new, more inclusive and flexible frameworks for cooperation and participation to address environmental challenges, particularly along the upper section of the river. The Kahnekarónnion (River) Strategy was launched in 2023 to help address this need, with the overarching goal of facilitating inclusive and equitable communication and collaboration along the St. Lawrence River and beyond. Here we describe the development of this collective and its basis in Haudenosaunee approaches to collaboration. We also outline some emergent guiding principles and their alignment with best practices identified in the literature on participation and knowledge co-production.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.005 |
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".