Bibliographic record
Abstract
It is broadly recognized that local knowledge and values should play a prominent role in natural resource decision-making. This research was based on the concept of ecosystem services (ES), which are the ecological processes through which nature provides benefits to people. A primary methodological research goal was to test an interview protocol to solicit the verbal articulation, spatial identification and a quantitative measure of local monetary values, non-monetary values and threat intensities associated with marine ES. This research identified and characterized a wide range of ways in which people value marine ecosystems in the Regional District of Mount Waddington in British Columbia, Canada to inform an ongoing marine spatial planning process. A total of 30 semi-structured interviews were conducted based on non-proportional quota sampling to target interviewees from across the district who have a variety of marine-related occupations. The interview protocol was successful in eliciting emotive expressions of the intangible benefits and values pertaining to ES. All interviewees verbally identified these benefits and values, but some (30%) refused to assign quantified non-monetary value to specific locations and others (16%) chose not to identify specific locations of non-monetary importance. Given that the spatial quantification of non-monetary values was not broadly acceptable, it is recommended that these research findings and methods complement deliberative processes to enable decision makers to more fully consider stakeholder’s non-monetary values and threats associated with ES. When explaining values and threats across the seascape, respondents bundled various services, benefits, and values associated with ecosystems. For articulating specific values, many used metaphors quite different form the implicit ES metaphor of nature as service provider. This protocol did not fully crowd out these alternative metaphors. Based on the spatial analysis, there was significant overlap among all three pair-wise comparisons of monetary values, non-monetary values, and threat intensity values. People tended to assign greater monetary and non-monetary value closest to inhabited locations. Employment in salmon aquaculture, the most divisive marine issue in the region, correlated with the perception that the ocean does not face environmental threat associated with this industry.
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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 | 0.004 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".