Visibility of Sustainable Development Efforts: Assessment of Kentucky Trail Towns
Bibliographic record
Abstract
Many communities work on trail projects as opportunities for sustainable development. Kentucky Trail Towns are accomplishing certification through a systematic state-wide program that guides communities, established in geographically advantageous locations, through a resource-based approach to community and economic development. Kentucky Trail Town communities proactively assess and explore physical assets, economic feasibility, and marketing strategies to capitalize on trails associated with adventure tourism. Since 2013, 17 Kentucky Trail Town certifications have been celebrated, particularly in and around publicly protected and managed areas, such as national and state parks and trail systems among other types of recreational destinations pertaining to adventure tourism. This study systematically evaluated 16 Kentucky Trail Towns with a focus on wayfinding systems and communication of trail-related amenities and services emphasized in the program guidelines. This study found the effectiveness of trail towns in Kentucky to be in its infancy from a short-term perspective. As a long-term strategy, Kentucky Trail Towns should continue their efforts to sustain and strengthen their relationships between core areas of town and major destination trails along with implementing visible indicators throughout the community. Further planning and design considerations can complement existing trails to enhance visitor experiences while also supporting the host community to preserve their landscape characteristics and place identity.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".