Fractals: A Natural Model Technology Supported Learning Outside
Bibliographic record
Abstract
Fractals are repeating, recursive, diminishing patterns often found in nature. Imagine a tree, starting with the trunk, held strong by its roots that expand into the soil in support of the branches, stems and twigs that extend up and out. As the tree grows in every direction, the branches and roots are increasingly finer, more delicate, fluid and growing versions of the original trunk. New schools, inquiry projects, and this paper share this model of growth. All start with a big idea, extend out in divergent directions and uncover new questions as the inquiry lives and grows. This paper is part of a larger inquiry project looking at developing a land-based middle school at the new school I am fortunate to help co-create. That school is Mill Bay Nature School, on Vancouver Island, in British Columbia, Canada. To support this inquiry, these chapters attempt to summarize a small part of the current literature on pedagogical best-practice and the use of technology in education. The large central trunk themes arising from the literature on current pedagogy include experiential learning, land based learning, place-based learning, place-conscious learning, Indigenous pedagogy and its connection to 21st Century Learning. Other branches of literature reviewed include a view of the accelerated use of technology in education, student and teacher engagement, the dynamic needs of modern learners, and the current focus on twenty-first century skills. The sources are primarily published in the past five years. As much as possible focus on the local context, issues and opportunities specific to the Province of British Columbia, Vancouver Island and ultimately my own school. This collection represents the serendipitous wonders that became the branches, stems and twigs of my inquiry my goal of bringing educational technology and learning outside together.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".