Heterogeneity in attitudes underlying preferences for genomic technology producing hybrid poplars on public land
Bibliographic record
Abstract
We investigate the public preference heterogeneity of planting genetically improved poplar trees for biofuel production on public land in western Canada. Using a sample of the public from British Columbia, Alberta, Saskatchewan, and Manitoba, respondents were asked to vote in a series of hypothetical referenda comparing the new, proposed forest policies with the current policy (base scenario). Proposed policies varied based on poplar breeding method (traditional, genomics, or genetic modification) and whether poplars may be used for biofuel production. A respondents’ segmentation framework with cluster analysis and probit model was applied to data of respondents to uncover the heterogeneity of public’s perception. The results of this study reveal that positive and negative perceptions about planting genetically improved poplar trees in the region create a division of respondents into Environmentalists, Knowledgeable, Challengers, and Supporters. Respondents from British Columbia and Manitoba are identified as Environmentalists and Challengers, respectively, of the new policy of planting genetically improved poplar trees on public land. Conversely, respondents from Saskatchewan and Alberta are identified as Supporters and Knowledgeable, respectively, of the new policy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".