Exploring attitudes and preferences toward species at risk in British Columbia
Bibliographic record
Abstract
There are 199 species at risk in British Columbia (B.C.). To elicit public support to conserve biodiversity, it is important to understand people’s attitudes and preferences toward species at risk. Here we examine how people perceive endangered species in B.C., how message framing shapes the attitudes toward the species, and whether implicit or explicit preferences determine willingness to pay for conservation. In Study 1 reported in Chapter 2, we presented three messages about sea otters to 623 residents in B.C., and measured the change in their attitudes toward sea otters using Kellert’s typology of basic attitudes toward wildlife. The messages were framed as either positive (as a keystone species), negative (resource conflict with First Nations’ fishermen in the West Coast of Vancouver Island), or neutral (biological facts). We found that the negative message promoted acceptance for managing sea otters and their habitats for use values (utilitarian-consumption, utilitarian-habitat), and for exerting control over sea otters (dominionistic). This shift in attitudes occurred even though the negative message was perceived as less convincing and believable than the positive or neutral messages. The positive message, on the other hand, decreased utilitarian-consumption attitudes. In Study 2 reported in Chapter 3, we evaluated people’s implicit and explicit preferences for four species at risk in B.C. (sea otter, American badger, caribou, and yellow-breasted chat). We found that explicit rather than implicit preference predicts willingness to pay for conservation of each species, and findings suggest that people apply the affect heuristic when judging species—species that are less liked may be perceived as riskier, and vice versa—. This finding holds for both residents in B.C. (n=55) and outside of B.C. (n=463). The results from the two studies highlight the importance of attitudes, messaging, and preference when designing conservation campaigns and efforts.
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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 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".