Popularity, Technology, and the Pacific Crest Trail: 20 years of Change
Bibliographic record
Abstract
abstract: For many, a long-distance hike on the 2,650+ mile Pacific Crest Trail (PCT) is the adventure of a lifetime. The federally designated National Scenic Trail passes through 48 Wilderness Areas in California, Washington, and Oregon on its way from Mexico to Canada. The trail experience on the PCT has been changing rapidly over the last 20 years due to two main factors: a four-fold increase in hikers attempting the whole trail each season; and hikers’ rapid adoption of digital technology like smartphones, GPS, and satellite messengers. Through a literature review and accompanying hiker survey, this study aimed to determine how these two factors have combined to alter the trail experience. Despite increased traffic on the trail, hikers appear to still be able to find ample solitude and a feeling of escape from society, and they reported being more likely to form lasting friendships as part of a “trail family�. However, increased traffic has altered many of the sensitive natural landscapes along the trail, contributed to the retirement of some iconic “trail angels� and led to increased conflict between subcultures within the community. Digital technology usage, particularly the use of smartphones and GPS-capable mapping apps, seems to be linked to decreased feelings of solitude, self-sufficiency, and escape. However, digital devices have helped democratize long-distance hiking by simplifying the logistics of long-distance hikes. Users of the devices also did not report reduced feelings of freedom or challenge from their hikes. Moreover, device users still felt that they were disconnecting with technology when hiking on the trail. Acknowledging both positive and negative effects of the changing trail experience, hikers can make more informed decisions about how to mitigate the negative impacts and maximize the positive impacts on the aspects of the trail experience they care the most about. (abstract)
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".