Public Legal Education Podcasting for Newcomers in Ontario: Affective Interventions in Participatory Action Research
Bibliographic record
Abstract
Due to the high cost of legal advice, language barriers, and other matters of social inequality, newcomers to Canada often have difficulty accessing legal information and resolving their legal problems. My Participatory Action Research (PAR) project aims to better understand the barriers newcomers face to accessing their legal rights, as well as to improve access to workers’ and tenants’ rights through the creation of Rights Bites, a legal rights audio podcast. Each podcast episode features interviews with newcomers, community workers, and lawyers, who share personal experiences and practical legal rights information about common housing and employment law problems. By applying affect theory as a lens for examining the podcast interviews, as well as the PAR process more broadly I argue that the complex expressions of anger, fear, distress, and pleasure displayed by my immigrant interviewees is a form of cultural citizenship, which reimagines belonging as a contested and ongoing project.
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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.030 | 0.015 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".