Landowner Perception of Information about Prescribed Fire: Influence on the Application of this Land Management Tool in the Southern Great Plains
Bibliographic record
Abstract
Prescribed fire is an important management tool on many rangelands. However, evidence that this tool is effective for mitigating multiple problems faced by landowners has not led to substantial increase in its adoption. Lack of knowledge about the safe application of this tool has often been cited as a reason for not applying it, which has led to calls for more education and outreach efforts to fill this knowledge gap. However, even when education is provided to landowners, adoption rates often do not increase substantially. When examining education improvement strategies, credibility often emerges as a primary determinant of information acceptance. Previous research indicates the relationship users have with a particular source and medium of information heavily influence their acceptance of the information. My research attempts to identify facets of information, other than credibility, that potentially influence information acceptance; these include: reliability, clarity, relevance, accessibility, and shareability. This research explores how those factors affect landowner perceptions about sources and mediums that disseminate information about prescribed fire. \nThe hypothesis is the perception of information and the users’ relationship with that source/medium plays a more significant role than previously thought. This hypothesis is tested using data derived from telephone interviews of key informants and online Internet-based survey of members of the Texas and South Western cattle Raisers Association and the Texas Wildlife Association. The results of this study provide guidance for government agencies and landowner entities, such as prescribed burning associations, for improving their information dissemination practices in order to enhance landowner perception and adoption of prescribed fire.
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.
How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 teacher head, 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".