Relating extension education to the adoption of sustainable forest management practices
Bibliographic record
Abstract
Family forest lands represent a vitally important economic, environmental, and social resource in the U.S. A study of family forest owners was conducted in Virginia in 2007 to determine the relationship between attendance at Extension Service educational programs and the adoption of sustainable forest management practices. A mail survey was conducted to 3435 randomly selected forest owners, with a usable response rate of 32%. Participation in educational programs was shown to be significantly related to higher levels of adoption for all seven categories of sustainable forest management practices studied. For example, in the woodland management category, participants in workshops offered through the Virginia Forest Landowner Education Program (VFLEP) adopted one or more specific practices at a rate of 94%, significantly greater than 83% for forest owners who attended other general educational programs, which in turn was significantly higher than the 75% adoption rate for forest owners who did not attend any educational programs. Two key indicators of sustainable forest management are the preparation and use of a forest management plan, and the use of professional technical assistance providers. For both of these categories participants in the VFLEP adopted at significantly higher rates, 41% and 73%, respectively.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".