Demographic differences in hiker cellular technology use in backcountry areas in Montana’s Custer Gallatin National Forest
Bibliographic record
Abstract
Technology remains an important part of outdoor recreation, ranging from the introduction of lighter materials in gear to new gadgets that improve the outdoor experience. Recently, advances in cellular technology and mobile devices have presented new opportunities for using mobile technology in backcountry areas. Applications ranging from public lands apps to GPS apps are a now a common find in outdoor recreation areas. Use of mobile technologies, such as cellular phones, can differ by demographic variables such as sex, age, and income. This presents a valuable opportunity to explore how and why demographics may shape the use of cellular devices while in the backcountry. This study examines technology use among hikers in Montana’s Custer Gallatin National Forest. Using data from an online survey, the researchers explored the importance of eight different uses of cellular technology while in the backcountry and analyzed how these responses vary by sex, age, income, and education categories. The results indicate cellular technology plays a varied, albeit often neutral or even unimportant, role in backcountry outdoor recreation situations. Notably, these experiences do vary by age, education, and income categories but, surprisingly, not sex. Important outcomes include new understanding of hiker use of cellular devices as cameras, wayfinding devices, and for information gathering while in the backcountry.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".