Comparing the Educational Preferences and Management Roles of West Virginia's Male and Female Woodland Owners
Bibliographic record
Abstract
Non-industrial private forest owners (NIPF) make up the majority of the landscape in the eastern United States. Historically NIPF owners have been treated as a homogenous group. This however does not adequately represent the diversity of this population's ownership objectives, management concerns, and land values that are important in understanding how to tailor educational outreach programs to this group. Butler (2008) called for the need to separate this large population into smaller populations that are more homogenous in order to better reach them with educational programs. To answer this call we divided NIPF owners into two distinct groups, male and female woodland owners. In this research, educational preferences and management roles of woodland owners in West Virginia were investigated for differences among these two groups of owners.;Utilizing a mail-based questionnaire, four counties in West Virginia were surveyed with the objective of gaining a better understanding of the female population of woodland owners and managers. Principal component analysis and logistic regression were used to analyze the data collected. Results show that management roles greatly differ between genders, however, educational preferences are not as clearly defined.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".