Design, arthrology and transtextuality in Seagle’s and Kristiansen’s It’s a Bird
Bibliographic record
Abstract
Abstract A theory of multimodality helps to explain how meaning is created by readers of comics at the level of the page and how readers situate themselves in relation to specific comics texts. In this article, I want to build on that theory in order to argue that in reading a comics text making meaning also involves the internal and external linkages that are continually made and unmade for and by readers. That is, it is not only at the level of a particular sequence of panels or page layout that meaning is made by comics creators and readers, but also through the connections that are made between various parts of the comics text itself (arthrology) and between the comics text and external texts (transtextuality). This article seeks to extend theories of multimodality to Thierry Groensteen’s arthrology and Gerard Genette’s transtextuality, concepts that help explain both the internal and external linkages created within comics texts. In doing so, I wish to explore some questions regarding how readers make sense of a complex comics text such as It’s a Bird by Steven Seagle and Teddy Kristiansen. What are the elements with which a reader must engage, at both the level of the page and the level of the text as a whole? How do these elements combine in the reading process? What accounts for the divergence of narrative meanings and textual interpretations between readers? In this article I examine how reading It’s a Bird involves making sense of the multimodal elements of each page, the arthrological connections between panels at both restricted and general levels, and the multiple kinds of transtextual connections between this text and myriad other texts.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".