In a Good Way: Braiding Indigenous and Western Knowledge Systems to Understand and Restore Freshwater Systems
Bibliographic record
Abstract
Insights from Indigenous and Western ways of knowing can improve how we understand, manage, and restore complex freshwater social–ecological systems. While many frameworks exist, specific methods to guide researchers and practitioners in bringing Indigenous and Western knowledge systems together in a ‘good way’ are harder to find. A scoping review of academic and grey literature yielded 138 sources, from which data were extracted using two novel frameworks. The EAUX (Equity, Access, Usability, and eXchange) framework, with a water-themed acronym, summarizes important principles when braiding knowledge systems. These principles demonstrate the importance of recognizing Indigenous collaborators as equal partners, honouring data sovereignty, centring Indigenous benefits, and prioritizing relationships. The A-to-A (Axiology and Ontology, Epistemology and Methodology, Data Gathering, Analysis and Synthesis, and Application) framework organizes methods for braiding knowledge systems at different stages of a project. Methods are also presented using themes: open your mind to different values and worldviews; prioritize relationships with collaborators (human and other-than-human); recognize that different ways of regarding the natural world are valid; and remember that each Indigenous partner is unique. Appropriate principles and practices are context-dependent, so collaborators must listen carefully and with an open mind to identify braiding methods that are best for the project.
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 | 0.057 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.012 | 0.035 |
| Scholarly communication | 0.019 | 0.035 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".