Sharing the hero's journey: blog posts from the Appalachian Trail.
Bibliographic record
Abstract
This chapter focuses on a hiking choice that, like the hero's journey, embraces difficulty. The researchers studied online blogs posted by thru-hikers on the Appalachian Trail (the AT). The term thru-hike describes a typically long-distance hike that traverses an acknowledged 'trail' from end-to-end. Thru-hiking the 2190 mile (3525 km) AT requires months of planning and effort and thousands of dollars to pay for related expenses. More than that, participants expect to undergo extensive physical and emotional hardship. The trail is challenging, often dangerous and fraught with uncertainty. It demands much of its participants, yet hikers are both willing and even eager to undertake those demands. Over 2000 hikers attempt a thru-hike on the AT annually (Littlefield and Siudzinski, 2012).
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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 | 0.001 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".