Character Through Outdoor Adventure Education? The (Delimiting) Hope of Modern Virtue Ethics
Bibliographic record
Abstract
Background: The long-held assumption of character development through outdoor adventure education (OAE) maintains some adherents; however, growing criticism calls into question its efficacy. Yet, current social/environmental crises signal the immediate importance of moral education. Purpose: This article highlights the importance of character in moral assessment and guidance and registers the need for an account of character relevant to OAE’s social/environmental aims. It suggests a modern virtue ethical lens as a philosophical and practical way forward for OAE’s moral educational mission. Methodology/Approach: This article searches the OAE literature for substantiating evidence (rational and empirical). Then, in light of this search, a virtue ethical account of character, including its development, and relevance to OAE, is outlined. Findings/Conclusions: Evidence for character development through OAE is nearly non-existent, and semantic, philosophical, and empirical critiques loom large. If OAE wishes to continue its moral mission, then an account of character—promisingly provided in virtue ethical theory(s)—that can withstand these critiques is needed. Implications: Applied to OAE, modern virtue ethical theory provides an account of character, copes with the critiques, and supplies socially/environmentally relevant curricular and pedagogical guidance for future practice, while likely delimiting our claim to character development.
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 | 0.004 | 0.006 |
| 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 | 0.023 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".