Bibliographic record
Abstract
Dramatic recent growth in comics research suggests that comics studies has matured as a field, perhaps even constituting an emergent discipline. Yet important questions about the nature of this field and how it relates to established academic disciplines remain unresolved. This introductory chapter examines the genealogy of comics studies and explores the relationship between theory and method as a proxy for the field’s “paradigmatic” status. Four theories of page layout are analyzed as examples of theorization in comics studies. Drawing on Robert T. Craig’s “constitutive metamodel” of communications theory, the chapter ultimately rejects <italic>both</italic> attempts to retread the path of established humanities disciplines such as English literature and film studies <italic>and</italic> arguments against disciplinarity as such, calling instead for a dialogic conception of academic disciplines that continually reflects on the differences through which they are constituted.
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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 | 0.016 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.019 |
| Science and technology studies | 0.008 | 0.035 |
| Scholarly communication | 0.033 | 0.032 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".