Bibliographic record
Abstract
Abstract Graphic History – the telling, teaching and understanding of history through comics – has been growing in classrooms and university spaces for more than two decades. However, in the discipline of history the engagement with comics is still rare. The articles in this special section of International Public History all focus on multiple facets of graphic history in public history, ranging from research to applications in classrooms, libraries, museums, archives, and cultural institutions. We are focused on the growth and potential for historical pedagogy and didactics to embrace a framework of critical visual inquiry – elevating analysis of images to a status equal to written text. With ever increasing discussions of misinformation and a need for critical analysis in historical pedagogy, didactics, and historical thinking, we see great potential for Graphic History with its rich legacy of visual narrative and exceptional popular success and appeal to add to Public History. This special section hopes to serve as an inspiration and a call to action, to encourage public historians, and historians in general, to think more critically about their historical pedagogy, research, and teaching. What are we including and what are we leaving out?
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 0.002 |
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".