Mismatches in salmon social–ecological systems: Challenges and opportunities for (re)alignment in the Skeena River watershed
Bibliographic record
Abstract
Mismatches between institutions and social–ecological systems (SESs) are one of the foremost challenges in natural resource management. However, while mismatches are often cited in the literature as a major challenge, empirical evidence of mismatches and their consequences is limited. This is particularly true for complex SESs, such as on the Pacific Coast of North America, where salmon drive interactions across multiple environments, jurisdictions, and scales. Here, I use the theoretical concept of fit to examine institutional alignment in a large-scale Pacific salmon SES, the Skeena River watershed in British Columbia, Canada. Utilizing Canadian federal environmental assessments as a proxy for colonial environmental governance institutions, I describe the common causes and consequences of mismatches between institutions and salmon SESs. This case study suggests that mismatches are threatening salmon sustainability and negatively affecting Indigenous People’s rights, livelihoods, and approaches to resource management and stewardship. I argue that improving social–ecological fit in salmon SESs will require new or revitalized forms of environmental governance that consciously fit the underlying social–ecological dynamics. While these findings are based on the Skeena River watershed, they may be generalizable to other salmon SESs in which mismatches between social and ecological processes and institutions exist.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".