Determinants of enrollment in public incentive programs for forest management and their effect on future programs for woody bioenergy: evidence from Virginia and Texas
Bibliographic record
Abstract
Several federal- and state-sponsored programs, including cost-sharing arrangements, tax incentives, and technical assistance programs, are available to forestland owners, aiming to encourage desired forest management practices and outcomes. However, enrollment rates in such programs are low, and trends of forestland parcelization hint at an even smaller enrollment rate in the future. Therefore, it is important to understand how socioeconomic attributes of forestland owners and past experience with such programs affect the likelihood of enrollment in public incentive programs. Among others, this will help us provide tailored information to forestland owners who are less likely to use these opportunities through extension and outreach programs. Towards this end, we conducted a survey of 1800 forestland owners in Virginia and Texas. Our recursive partitioning, logistic regression, and Cochran–Armitage trend test results suggest that forestland owners who are less likely to enroll in such programs have relatively smaller forestland acreage, a lower level of education, and shorter land ownership tenure. We also find that forestland owners with experience in public incentive programs tend to attach higher importance to potential programs, including those that do not directly benefit them. We also identify threshold values for explanatory variables such as acreage and tenure length.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".