How Visual Narratives (Comics) Can Increase Literacy, Decrease Bias, and Highlight Stories of Social Justice
Bibliographic record
Abstract
How can creating a simple stick figure comic help us tell — and deeply listen to — true stories of social injustice and practice anti-oppression strategies? More specifically, how can creating a series of stick-figure comics help learners enhance their understanding of the Indigenous Peoples’ testimonies in the Truth and Reconciliation Report (TRC, 2015)? In my experience, stick-figure visual narratives can help participants tell stories of social injustices and practice ways that might restore right relations. In this paper, I provide a background story and a literature review in describing the rationale and method of using this approach to teach social justice concepts and rehearse pro-social interventions. I conclude with a detailed lesson plan for using the social-justice comics method for visually presenting the TRC 2015 report. Comment l’acte de dessiner un bonhomme allumette peut-il nous aider à raconter – et à écouter très attentivement – des histoires vécues d’injustices sociales et à adopter des stratégies contre l’oppression? Plus particulièrement, comment des apprenants, en créant une série de bonhommes allumettes, peuvent-ils mieux comprendre les témoignages des Autochtones inclus dans le rapport de la Commission de vérité et de réconciliation (2015)? D’après mon expérience, les récits visuels en bonhommes allumettes aident les participants à raconter leurs histoires d’injustices sociales et à mettre en pratique des moyens pour éventuellement rétablir des relations justes. Dans le présent article, je décris le contexte et les études qui sous-tendent le pourquoi et la méthode des bonhommes allumettes pour enseigner les concepts de justice sociale et exercer la pratique d’intervention sociale. En conclusion, je présente un plan de leçon indiquant comment utiliser la méthode des dessins à portée sociale pour représenter visuellement le rapport de la Commission de vérité et de réconciliation de 2015.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".