Diversifying Rural Economies with Natural Resources: The Difference Between Local and Regional OHV Trail Destinations
Bibliographic record
Abstract
With an estimated forty-four million riders, Off-Highway Vehicle (OHV) usage is one of the fastest growing forms of recreation in the United States. The National Survey on Recreation and the Environment suggests that this recreation and growth is largely occurring on public lands, most of which are situated in rural areas. OHV riders have been reported to have a consumer surplus ranging from US$ 25.51 to US$ 131.58 for recreational day trips, creating a potential lucrative market for rural communities wishing to diversify their economy. However, research has also found that OHV use can negatively impact natural resources and the experience of other non-OHV visitors. Given the potential positive economic impact, as well as the potential negative environmental and social impacts, it is important to identify which factors are most important for attracting OHV users that maximize positive outcomes and reduce negative outcomes. Additionally, for destinations seeking to establish or expand OHV opportunities to attract substantial users, information is needed on the differences between trail systems that attract only local markets, versus regional or national markets. Results suggest that the primary factor distinguishing between local and regional trails systems is the number of miles of trail, with secondary considerations being trail design and management policies. Keywords: Off Highway Vehicles (OHV), motorized recreation, rural natural resources, trail characteristics
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".