Bibliographic record
Abstract
This thesis is the story of an educator and the unfolding narrative of SelfDesign, a way of thinking about learning and education. It documents and examines the development of SelfDesign as an emerging learning process and outlines its integral principles. I started SelfDesign in 1983 responding to the request of my five-year-old daughter, who, after two weeks in kindergarten, wanted to quit school so that she could learn the things she wanted to learn. Modeled on my insights of my daughter learning how to speak naturally, I designed an integral learning program based on choice, conversation, mutual respect, curiosity, discovery and enthusiasm. Evolving beyond current assumptions about learning as an artifact of instruction, SelfDesign redefines learning as an ontological process of maturation and development, emerging through conversations and relationships. Based on twenty-five years of observing children learning in freedom beyond the paradigm of schooling, I realized that learning is an epistemological process integral to living and unfolding human potential as a learning organism. SelfDesign encourages introspective awareness and provides learners with methodologies, maps and models for designing their learning process. Every learner in SelfDesign creates an individual curriculum ensuring their right to design their own learning, and their learning arises from an innate sense of integrity. The program was called Wondertree (1983-2009 with a dozen young learners), then Virtual High (1993-1997 with 35 teenagers), and is now SelfDesign Learning Community (2002 - present with 1400 learners in a province-wide online program). This thesis, relying on data from the interviews of 27 randomly selected SelfDesign graduates (average age 27.5), identifies themes that SelfDesign encourages: freedom, self- responsibility, learning how to learn, becoming known and seen as an integral individual, feeling empowered as the author and authority of one’s life, experiencing the synergistic effect of respect and love in a community, discovering the praxis of learning as living in co-inspiration, and discovering one’s purpose. The graduates share a sense that SelfDesigning is a transformational experience and an empowering process that has inspired each of them to become enthusiastic, authentic lifelong learners.
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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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.080 |
| Scholarly communication | 0.016 | 0.025 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".