Bibliographic record
Abstract
The 2015 publication of Nick Sousanis’s graphic dissertation, Unflattening, opened doors for artist-scholars who challenge conventional research methodologies by producing graphic dissertations, graphic research, and comics-based publications in academic, scientific, and medical journals. Unflattening came out one year after I began a PhD program in cultural studies at the University of California, Davis, with a proposed graphic dissertation of my own. In this essay, I will discuss how my intended project, a graphic narrative about my maternal grandmother and her experiences of the Second World War in Germany, became a graphic memoir—an intersectional feminist Bildungsroman that explores themes of transgenerational memory, displacement, and childhood sexual abuse. As an astute scholar in my cohort put it, “You’ve found a new way of ‘doing’ psychology and history.” How is the very particular kind of subjectivity, a seeing from the ground up, or from a “snail’s eye view,” engendered by the comics form, useful for contemporary decolonial scholarship? In addition to writing about my graphic memoir, Queen of Snails (forthcoming by Graphic Mundi, an imprint of Penn State University Press in 2022), I will interview Kay Sohini, a PhD candidate at Stonybrook, about Unbelonging, her graphic dissertation in progress, Helen Blejerman (Lulu La Sensationelle, Presque Lune Editions, 2014), and Sarah Lightman (Book of Sarah, Myriad Press and Penn State University Press, 2021). How has the process of creating their graphic narratives changed their approaches to research? What have they learned by employing drawing and writing in crafting works that include autobiographical elements? How might some of these processes be useful to scholars seeking to unpack intersectional issues of transgenerational trauma, misogyny, and racism?
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.020 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.052 | 0.012 |
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".