Understanding residents’ desired approaches to manage forest access roads: a case from northeastern Ontario, Canada
Bibliographic record
Abstract
Managing roads and access on publicly owned forested lands should include assessments of the public’s views of desirability for different approaches. To assess the public’s views, we explored northeastern Ontario residents’ desirability ratings towards approaches that remove, deactivate, or close forest access roads. From a social survey, respondents, on average, rated every restrictive approach as undesirable. From a principal components and cluster analysis, we identified four groups of respondents (open roads, road deactivation, road closure, and sign-based road closure supporters) that differed in their desirability ratings for the approaches. Comparisons of group members by the types and intensity of outdoor recreation use, attitudes towards roads and management, environmental value orientations, and sociodemographic characteristics revealed expected and important differences. On the one hand, almost one half of respondents had high levels of undesirability ratings towards any restrictive approach, suggesting that attempts to restrict road access might be met with stiff opposition. On the other hand, the presence of groups that support road deactivation, sign-based road closures, and general road closures suggests that many other residents might be willing to accept some access restrictions on roads. Consequently, managers must be aware of the heterogeneity in views about access when planning roads and access.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.017 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".