One-Size Does Not Fit All—A Networked Approach to Community-Based Monitoring in Large River Basins
Bibliographic record
Abstract
Monitoring methods based on Indigenous knowledge have the potential to contribute to our understanding of large watersheds. Research in large, complex, and dynamic ecosystems suggests a participatory approach to monitoring—that builds on the diverse knowledges, practices, and beliefs of local people—can yield more meaningful outcomes than a “one-size-fits-all” approach. Here we share the results of 12 community-based, participatory monitoring projects led by Indigenous governments and organizations in the Mackenzie River Basin (2015–2018). Specifically, we present and compare the indicators and monitoring methods developed by each of these community-based cases to demonstrate the specificity of place, culture, and context. A scalar analysis of these results suggests that the combination of core (common) indicators used across the basin, coupled with others that are meaningful at local level, create a methodological bricolage—a mix of tools, methods, and rules-in-use that are fit together. Our findings, along with those of sister projects in two other major watersheds (Amazon, Mekong), confront assumptions that Indigenous-led community-based monitoring efforts are too local to offer insights about large-scale systems. In summary, a networked approach to community-based monitoring that can simultaneously engage with local- and watershed-level questions of social and ecological change can address gaps in knowledge. Such an approach can create both practices and outcomes that are useful to local peoples as well as to those engaged in basin-wide governance.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".