Bibliographic record
Abstract
Community-based learning initiatives have the potential to have a meaningful impact on participants. When integrated into an academic setting, such experiential learning opportunities can initiate transformative learning within students and the broader community. Through a self-reflexive approach, this essay describes one such first-hand experience from a Walls to Bridges class, offered through Wilfrid Laurier University and facilitated in a Canadian Federal Prison. The learning model utilized within this class has the capacity to deeply engage students in ways where traditional classroom methodology falls short. Institutionalized education can learn a great deal from this model, which values diversity and community building, and which centralizes voices that are often absent or marginalized in academic settings. This essay examines the nature of a Walls to Bridges class as it compares to traditional educational experiences. The essay explores current, dominant educational paradigms that are influenced by capitalistic values and can perpetuate power imbalances and systemic barriers, while also highlighting alternatives to traditional education models. Teaching methodologies, such as collaborative rather than competitive learning, circle pedagogy, the creation of a safe classroom space, power redistribution, and creative means of critical classroom discussions, are celebrated as opportunities for deep learning.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.047 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".