Bibliographic record
Abstract
Outdoor adventure education (OAE) research has long aimed to explain and understand the inner workings of its programs. However, many questions remain, and the search for sharper methodological tools with which to deepen our understanding of OAE continues. This article is a collaborative autoethnographic investigation of the unpredictable and difficult to measure nature of wilderness educational expeditions (WEEs). It is a reflexive journey of storytelling and critical analysis that demonstrates the power of story-based research as method. The findings indicate that conventional approaches to WEE research are limited in their capacity to fully understand and explain the inner workings of WEEs. We argue that practitioners need to “trust the journey” to elicit learning that comes from responding to encounters with people and place. Furthermore, we suggest that quests for a sequenced “journey recipe” are unrealistic and do not honor the philosophical and pedagogical foundations of OAE. Finally, a case is made for alternative, rigorous research approaches to be embraced to gain richer and more nuanced understandings of the wonderfully diverse experiences that make up WEEs.
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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.033 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".