Human Dimensions of Biodiversity Conservation in the Interior Forests of British Columbia
Bibliographic record
Abstract
Generally, studies on biodiversity conservation have focused on topics within the natural sciences, such as species and ecosystem concerns. However, an understanding of the human dimensions of biodiversity conservation is lacking. To address this gap, a study was undertaken in the Robson Valley in east-central British Columbia in 2001 to document stakeholders - understanding and perceptions of biodiversity issues, examine potential trade-offs associated with conservation, and provide decision makers with insight concerning the acceptability of potential forest management scenarios. A mail survey was used to collect data from residents of British Columbia and two groups of recreationists. Results show that stakeholders are diverse in their perceptions and knowledge related to biodiversity conservation. A choice experiment was used to examine trade-offs inherent in conserving biodiversity at the landscape level. The choice model showed that respondents preferred options that emphasized biodiversity conservation, and that Robson Valley residents had different preferences than the respondents in the other subsamples. Several potential forest management scenarios were simulated using the choice model results. The potential for future research, and ideas for improving the model, are discussed.
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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 | 0.000 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".