Bibliographic record
Abstract
Abstract Growing a nature kindergarten that can flourish takes a community, careful planning, and sustained support. In 2011, the Sooke School District in British Columbia, Canada undertook the project of creating a nature kindergarten when outdoor programs of this kind did not exist in the Canadian public school system. Inspired by the well-established forest school and nature preschool models in northern Europe, a program to take 22 kindergarten students outside into nature every morning, regardless of the weather, was developed. This article explores how a unique framework and set of guiding principles were co-created by a diverse advisory committee. It also describes how the hiring, education, and ongoing support of the program's two educators — a kindergarten teacher and an early childhood educator — became critical to its success. The article offers an overview on steps taken, including how the idea was born, working within the public school system, building a framework and principles, hiring and education, preparing the educators, learning from our first year, ongoing support, and remaining questions. The authors’ intention is not to articulate best practices, but to share key aspects of the program's development and implementation phases that allowed the nature kindergarten to thrive over the last 5 years.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".