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Record W4381126076 · doi:10.58680/rte2022318632

“Swirling a Million Feelings into One”: Working-Through Critical and Affective Responses to the Holocaust through Comics

2022· article· en· W4381126076 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueResearch in the Teaching of English · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicComics and Graphic Narratives
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComicsThe HolocaustNarrativeFeelingCritical literacyPsychologyIdentity (music)Representation (politics)LiteracyCitizen journalismSociologyAestheticsPedagogyVisual artsSocial psychologyPoliticsLiteratureArt

Abstract

fetched live from OpenAlex

Drawing on perspectives from cultural studies, affect theory, and critical literacy, this article explores comics made by three eighth-grade students in response to Art Spiegelman’s Holocaust memoir Maus. Students’ comics were developed through participatory research alongside their classroom teacher, a research team, and teacher candidates from a local university. These three students, Stella, Maisie, and Naomi, reacted strongly to the content of Maus and the comics medium, and raised questions around identity, representation, and the legibility of their often-intense emotional responses. We trace their affective engagements to explore how comic-making allowed students to represent feelings that are often difficult to make visible in school spaces. Our analysis highlights how affective critical literacy orients teaching and research toward working-through rather than resolving complicated emotions, allowing educators to recognize unanswered questions as forms of critical engagement.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.351
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.171
GPT teacher head0.394
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it