Bridging Indigenous and Western sciences in freshwater research, monitoring, and management in Canada
Bibliographic record
Abstract
Abstract Mutually respectful and reciprocal relationships between people and their environment is a central tenet of many Indigenous worldviews. Across the Americas, this relational connection is particularly evident when it comes to freshwater ecosystems. However, there are numerous threats to these central relationships between Indigenous peoples and their environment. Using all available ways of knowing to conserve, prioritize, and restore relationships between Indigenous peoples and the environment they live in, and are a part of, is critical. Despite legislative requirements and policy commitments, developing and implementing inclusive approaches that bridge multiple ways of knowing remains a challenge. This systematic map examines the extent, range, and nature of published case studies that seek to bridge Indigenous and Western sciences in ecological research, monitoring, or natural resource management across Canada's freshwater aquatic ecosystems. A total of 74 Canadian case studies from 72 articles were included in the systematic map. There were 30 distinct species of focus across the collection of case studies. This systematic map highlights the diversity of ways knowledge systems can be woven, but that the application of these approaches is limited to some key regions (the Pacific and northern regions) and species (whitefish and salmon). The extent and nature of information provided with regards to demographics (e.g., gender, age) of Indigenous knowledge holders contributing to the studies varied widely and in general was poorly reported. Across all of the case studies included in the systematic map there were 78 distinct Indigenous knowledge systems represented. Fifteen different methodological approaches were identified with community‐based participatory research being the most prevalent approach. The presence and diversity of Indigenous methodologies employed was also notable and was greater as compared to a previous study of Canada's coastal marine regions. Collectively, these findings point to a potential emerging transformation in research focused on freshwater ecosystems, habitats, and species to a practice that elevates the role of Indigenous communities, centres Indigenous science and knowledge, and is informed by Indigenous ways of being and doing.
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 | 0.022 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.024 | 0.051 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".