How Unschooling and Ungrading Has Helped Me In Law school
Bibliographic record
Abstract
I graduated high school and moved across the world from Toronto to London when I was eighteen to begin a three-year LLB program. I am now in my second year and have spent time reflecting on the way I grew up. I was unschooled for kindergarten and did not attend school until I was in grade one. Unschooling is a learner centre democratic approach where the learner is empowered and entrusted to make their own decisions. When I was unschooled, I was able to do what I wanted and how I wanted every day. One day I woke up and wanted to write a story and the next I wanted to learn how to do a cartwheel. The only person I had critiquing my work was myself, and that was enough to motivate me to keep learning. When I started public school, it was a foreign concept to me that I would be told what to do, how to do it, and that I was going to be assessed on how well I did it. Eventually it became difficult to be proud of myself or satisfied with my work until I got the validation from a high grade and assignments became stressful rather than enjoyable. Furthermore, I felt like it created a competitive environment between classmates and created a toxic space for young children to grow up in. Ultimately, my independence and self-confidence were adversely affected. The goal of this autobiographical narrative research paper is to share my school experience from primary school to law school, and to share how I continue to learn things without grading, which is something that has stuck with me given my unschooling experience.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.050 | 0.032 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.014 |
| 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".