Bibliographic record
Abstract
This presentation emerges from our experiences teaching an online, asynchronous children’s literature graduate course. Across its six-year history, teacher-participants have responded variously to the graphic novel Skim. Recently, when confronted with an image in the novel depicting a kiss between a female high school student and her female teacher, some course members focused solely on the kiss, seeming to dismiss the text entirely. To think through this unprecedented occurrence, and to consider its philosophic and curricular constraints/possibilities, we draw on the thinking of Julia Kristeva and those who interpret her work. Specifically, we are thinking about the role of discomfort in leading to Kristeva’s abjection—or, a casting out of the perceived other—and Sonja Arndt’s “dominant need to know” as possibly protecting against dwelling within the uncertain chaos of discomfort and the resulting redefinition of self. This presentation contributes to the ongoing conversation around pedagogies of discomfort. We also consider the distinct temporal experience of reading a graphic novel and suggest that the unique temporality of both the assigned reading and teacher-participants’ lives may encourage/discourage dwelling in discomfort. In closing, we discuss possible curricular and pedagogic shifts we might make to open spaces for dwelling with/in discomfort.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.025 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".