Credentialing Standards for Teaching Outdoor Activities: An International Comparison
Bibliographic record
Abstract
There is little research on the process for credentialing teachers of outdoor recreation activities. This research used an explanatory mixed-method research design to understand the credentialing requirements for becoming an outdoor instructor. Following a census and constant comparative analysis of 155 credentials from 62 credentialing organizations in Australia, Canada, New Zealand, United Kingdom, and the United States, the second phase of research explored the phenomenon of credentialing in outdoor education using a maximal variation sampling strategy. Results emphasized a prevalence of organizations in all countries and enormous variety in outdoor instructor credentialing requirements. As a result, a typology of the requirements for becoming and outdoor instructor was developed. A series of common themes emerged across all credentials; however most credentials utilized a unique set of standards for screening, training, and evaluating instructor candidates. Findings also demonstrated contradicting evidence for human capital theory, credentialist theory, and signaling theory, and the multiple rationales for the purpose of credentialing led to the exploration of a new theory of credentialing based on Bronfenbrenner's ecological systems theory. The similarities and differences between outdoor credentials were explained by multiple factors including: geography, activity, philosophy, culture, politics and industry. Implications include a need for better transparency of training and assessment strategies and increased sharing of information among organizations and educational disciplines.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".