Comic Book Superheroes: Proposals for Intervention through Multiliteracies Pedagogy
Bibliographic record
Abstract
This doctoral thesis discusses the theme “Comic Book Superheroes: an application in light of the Pedagogy of Multiliteracies, according to the BNCC (2017)”. The proposal in question had as its general objective the involvement of first-year high school students, from a public school in the state of Goiânia-GO, in the creation of a comic book superhero who intervenes in the Brazilian nation, aiming at the dissolution of the most immediate social problems. This superhero was structured on a digital platform, with a view to the inference of the students in the virtual world, implementing the Pedagogy of Multiliteracies (GNL, 1996). The projectivity was based on three expository dialogued classes that followed the teacher's observation of the students' involvement in the proposed activity. Initially, the methodology was bibliographic, and then the qualitative scope was used, culminating in Participant Observation, which drew on the theoretical contributions of Bakhtin (1988, 1997, 2006, 2010, and 2013) and his dialogical orientation, as well as Foucault (2000, 2008, and 2009) and his postulates used in Discourse Analysis. Among the other researchers, we also drew on studies by Geraldi (1996, 1997, and 2015), Santos (1997 and 2014), Freire (1996), Eco (2015), Laval (2004), Masschelein & Simons (2014), Wolf (2019), Rojo et al. (2012, 2015), Kalantzis et al. (2020), Dudeney et al. (2016), Bacich et al. (2015, 2018), Filatro (2018), Sibilia (2016) and others for analytical studies of language, utterance, discourse, neoliberalism in schools and didactics related to teaching and learning. In the context of superheroes, we brought discussions based on Campbell (1990), Vogler (1998), Irwin (2005) Weschenfelder (2011), Hallett (2007) and others. The analytical research of the texts produced concluded that the students' involvement in the creation of a comic book, in a virtual environment, was greater than in the viabilities of conventional literacy, in addition to conceptually establishing that the Pedagogy of Multiliteracies, discussed by Rojo (2012) and proposed by the BNCC (2017), also linked to the cognitive biases of the DCGO – Expanded, can be used in the classroom, even with interposed difficulties.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".