Time Frames: Graphic Narrative and Historiography in Richard McGuire’s Here
Bibliographic record
Abstract
Abstract | Visual literacy has long been important as a way of reading images beyond mimetic illustration. It also allows the reader to tap into a logic of representation in order to create different representations and narratives. In this essay I argue that images provide crucial temporal complexity to the study of narrative, with particular resonances for narrative historiography. The complex temporality of the image, especially the graphic narrative or comic, points toward a historical time which may be neither linear nor causal. Moreover, images demand interaction from the reader, but offer many avenues of interpretation, suggesting that the reader pay attention to their own constructions of meaning and practices of narrativizing.Résumé | La litéracie visuelle a longtemps été importante comme façon de lire les images hors de simple illustration mimétique. Elle permet aussi le lecteur d’entrer dans une logique de représentation pour créer des différentes représentations et des différentes narrations. Dans cet essai, je soutiens que les images fournissent une cruciale complexité temporale pour l’étude des narrations, avec valence particuliaire pour l’historiographie narrative. La temporalité complexe de l’image, surtout la narration graphique ou bande dessinée, indique une temporalité historique qui sera peut-être ni linéaire, ni causative. Par ailleurs, les images exigent l’intéraction du lecteur, et au même temps ils offrent plusieurs avenues d’interprétation, ce qui attire l’attention du lecteur à ses propres habitudes de lecture et à ses propres narrations construits.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".