Sea otters, social justice, and ecosystem‐service perceptions in Clayoquot Sound, Canada
Bibliographic record
Abstract
We sought to take a first step toward better integration of social concerns into empirical ecosystem service (ES) work. We did this by adapting cognitive anthropological techniques to study the Clayoquot Sound social-ecological system on the Pacific coast of Canada's Vancouver Island. We used freelisting and ranking exercises to elicit how locals perceive ESs and to determine locals' preferred food species. We analyzed these data with the freelist-analysis software package ANTHROPAC. We considered the results in light of an ongoing trophic cascade caused by the government reintroduction of sea otters (Enhydra lutris) and their spread along the island's Pacific coast. We interviewed 67 local residents (n = 29 females, n = 38 males; n = 26 self-identified First Nation individuals, and n = 41 non-First Nation individuals) and 4 government managers responsible for conservation policy in the region. We found that the mental categories participants-including trained ecologists-used to think about ESs, did not match the standard academic ES typology. With reference to the latest ecological model projections for the region, we found that First Nations individuals and women were most likely to perceive the most immediate ES losses from the trophic cascade, with the most certainty. The inverse was found for men and non-First Nations individuals, generally. This suggests that 2 historically disadvantaged groups (i.e., First Nations and women) are poised to experience the immediate impacts of the government-initiated trophic cascade as yet another social injustice in a long line of perceived inequities. Left unaddressed, this could complicate efforts at multistakeholder ecosystem management in the region.
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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 | 0.001 | 0.002 |
| 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 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".