Get the Arts Outdoors: Merging Arts and Nature in Outdoor Education at the YMCA
Bibliographic record
Abstract
As part of my SASAH experiential learning requirement, I worked at the YMCA Cedar Glen Outdoor Centre during the Fall/Winter term of 2021/22. Although I had previously worked at the outdoor centre as an educator teaching groups, this year was a different experience due to the new responsibilities and unique learning opportunities presented by the onset of the COVID-19 pandemic. Throughout the year, I was responsible for the creation of lesson plans merging the arts and sciences in an outdoor setting according to the curriculum requirements of Ontario’s Ministry of Education. Moreover, I implemented inclusive learning options for each unit to ensure education can be equitably provided to all students attending the centre. Other responsibilities included adding curricula requirements from various subjects to entice teachers into using outdoor programming for their students’ learning, along with making self-guided activities to adapt to the pandemic while enabling the community to use the outdoor centre. In doing so, I learned that outdoor education is valuable to the learning and development of children and youth, however, it is underutilized due to time constraints, teacher hesitancy, and limited knowledge about the field. My work during this experiential learning sought to address these challenges and with the help of my team at the Y, I grew as a person, developed essential skills such as collaboration, communication, and problem-solving, and gained invaluable career-related experience in the field of education.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".