Characterizing Non-Industrial Private Forest Landowners' Forest Management Engagement and Advice Sources
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Non-industrial private forestland (NIPF) owners have options for engagement by following management strategies that reduce wildfire risk on their forestlands. Forest management engagement is a broad term with underlying categories and management implications. To better understand these categories, we examine interview data on the engagement of forest landowners from a case study of private forestland owner perspectives in northeast Oregon, USA. NIPF landowners outline two types of forest management engagement, one for property and one for community-focused forestland management. NIPF owners describe actions for engagement in public forestland management and how these actions differ from engagement in private management. Additionally, NIPF owners establish barriers to engagement in both public and private forestland management. Our findings can be used to better identify unengaged private forestland owners in the U.S. West, informing the design and implementation of extension and outreach for NIPF owners.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it