Bibliographic record
Abstract
The Wahkohtowin class was held in Saskatoon throughout early 2014. Wahkohtowin brought together prisoners, students, and professors, in order to critically examine Canada’s “justice” system. For the author, participation in this class led to deep learning, or what ancient Greeks called Paideia. This article explores why Wahkohtowin led to deep learning. It concludes that the deep learning was attributable to four factors: the leaders created space for suffering to speak; course participants were racially, culturally, and educationally diverse; the pedagogy was relationally Socratic; and participant reflections were aimed at action, in addition to understanding. Throughout the article, Wahkohtowin is compared with the author’s experience of law school in order to highlight why law school courses rarely result in Paideia.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.907 | 0.921 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.765 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.781 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; both teacher heads agree on what is shown here.
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".