Exploring diversity in expert knowledge: variation in local ecological knowledge of Alaskan recreational and subsistence fishers
Bibliographic record
Abstract
Abstract Local ecological knowledge (LEK) of resource users is a valuable source of information about environmental trends and conditions. However, many factors influence how people perceive their environment and it may be important to identify sources of variation in LEK when using it to understand ecological change. This study examined variation in LEK arising from differences in people’s experience in the environment. From 2014 to 2016, we conducted 98 semi-structured interviews with subsistence fishers and recreational charter captains in four Alaskan coastal communities to document LEK of seven fish species. Fishers observed declines in fish abundance and body size, though the patterns varied among species, regions, and fishery sectors. Overall, subsistence harvesters provided a longer-term view of abundance changes compared with charter captains. Regression analyses indicated that the extent of people’s fishing areas and their years of fishing experience were relatively important factors in explaining variation in fishers’ perceptions of fish abundance. When taken together, perspectives from fishers in multiple regions and sectors can provide a more complete picture of changes in nearshore fish populations than any source alone. These findings underscore the importance of including people with different types of expertise in local knowledge studies designed to document environmental change.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".