What do comics want? Drawing lived experience for critical consciousness
Bibliographic record
Abstract
Abstract This article presents a reflection on drawing autobiographical comics as a method of engagement with critical theory and the potential for illustration education. I suggest that drawing and sharing autobiographical comics might be used to engage illustration students to think critically about identity, representation and power. To illustrate this approach, I present my own practice-based research project that used comics-making as a method to make sense of queer ways of being in childhood – ways of being that may have been discounted, ignored or suppressed within a dominant heteronormative culture. The intention was to evoke a playful mode of drawing that might queer my illustration practice while braiding childhood memory with critical theory. As educators, to get our illustration students to think critically, we might start with the students’ own lived experience and enlist the potency of comics to visualize their stories as resilient and instructive counternarratives. I suggest that drawing comics might be reframed as a performative space for playing with our stories to understand the historical and socio-economic forces that shape our lives and identities. Through making and sharing autobiographical comics, we engage in a transgressional strategy that uses story and drawing as transformative tools for Freirean critical consciousness.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.045 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".