International Perspectives in Outdoor Education Research
Bibliographic record
Abstract
In November 2013, a group of about 120 outdoor education researchers gathered at the University of Otago in Dunedin, New Zealand. We had come together at the 6th International Outdoor Education Research Conference (IOERC) from many corners of the world. Without such events, it can be easy for us as outdoor education researchers (and practitioners) to limit our perspective and inquiry to the people and places of our home range, forgetting that outdoor education is happening in some shape or form in most countries around the globe. The biennial IOERC has become a time of renewal and sharing of ideas for many researchers, a time to be inspired by colleagues’ research, which in turn reframes our own research, and we start viewing and understanding it through a more diverse international lens. In Dunedin, we were selected to co-convene the 7th IOERC and chose to host it at Cape Breton University, on Unama’ki (Cape Breton Island) in Nova Scotia, Canada. The purpose of this special issue of the Journal of Outdoor Recreation, Education, and Leadership (JOREL) is to showcase examples of international research, some of which was presented at the 7th IOERC, where 150 researchers from 17 countries lived and learned together. Subscribe to JOREL
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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 | 0.012 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.014 | 0.025 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".