Woodland Owners Motivations for Involvement in Landscape Scale Forest Stewardship
Bibliographic record
Abstract
West Virginia is mainly covered by forest, most of which is in the hands of private forest (PF) owners. The decisions taken about the management of these properties affect the landscape beyond their parcel boundaries. These forests provide ecological services to society, timber products and recreation. Keeping the forest healthy and productive is very important for the common good. Threats related to development parcelization, invasive species and pests are some of the challenges when trying to maintain forest coverage in WV. To be able to face these challenges it is necessary to plan at a wider scale than the individual parcel. Landscape scale forest stewardship has been thought of as a way to manage the forest in a multiple tenure scenario. Cross boundary collaboration and public -private partnerships are necessary to move in the direction of large scale forest management. Understanding the attitudes, actions and motivations of PF owners is critical to success in this task.;In this study we conducted a public opinion survey in five diverse areas of West Virginia located along the primary inter-state transportation corridors. This research was designed using similar methodology to a study by Finley et al. (2006), to identify the attitudes, motivations and barriers to cross-boundary cooperation of private forest owners in the selected areas. We conducted a survey that gave 293 usable responses. Using Principal Component Analysis and Logistic Regression, four significant predictors for willingness to participate in cooperative activities were obtained: 1) education, 2) management activities conducted in woodland properties, 3) sharing property, and 4) the barrier "no cooperation benefits." Also, two dependent variables "market jointly" and "walking tour" to measure the interest of PF owners in engaging with neighbors in education and planning were obtained. Findings suggest that those with a college degree or higher had more than twice the odds of being willing to participate in cooperative activities, those interested in "share" had five times the odds of being interested in cooperative activities than those than were not. Private forest owners doing management activities on their properties were more inclined to participate in cooperative activities. The barrier "no cooperation benefits" produced odds ratio less than one for both cooperative activity variables suggesting those with an unfavorable view of cross-boundary benefits are less willing to collaborate with neighbors.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".