Heroes for Change or Systems for Change? Is it time to reject heroism discourse? : A critical eye into a comic edutainment on SDGs
Bibliographic record
Abstract
This study seeks to extend observations on critical citizenship education by examining what the edutainment Comics Uniting Nations, which presents the 17 Sustainable Development Goals (SDGs), may tell us about the UN view of imagined agency and citizenship, and subsequently, its broader view of development. Given that the SDGs’ message within the comics targets a global audience, the research work in this thesis puts the comic Heroes for Change to the test by surveying how the minority community in Gaza, occupied Palestine feel and situate themselves in the SDGs’ universal message. This is done via interviews with representatives who work with the youth in Gaza at local NGOs. The main discursive and visual analytical tools are postcolonial critical literacy in international development initiatives, soft vs critical theories of citizenship, and superhero semiotic and panel rhetoric organization. The result of this work shows that while the comic uses a universal and convivial citizenship discourse, it misses being a bottom-up designed agenda and hence misses distinguishing between marginalized and ordinary citizens. Also, the superhero metaphor echoes a problematic aspect in opening space for critical thinking and challenging the status quo, which calls to spark further debate on the limitations/potentials of superhero discourse as a communicative tool for radical development/social change.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.032 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".