Our Pandemic Year: On the Comics Scholarship to Come
Bibliographic record
Abstract
This editorial article reflects on the past, present and future of The Comics Grid: Journal of Comics Scholarship. It discusses the challenges overcome so far, and discusses the tenth volume of the journal, corresponding to 2020, “our pandemic year”. The article presents the authors’ vision for the type of comics scholarship they would like to see in future volumes of the journal, calling for greater diversity and inclusion and for work which is ‘media-specific’ in at least three ways: firstly because the field’s focus is comics, in all their multifaceted diversity, complexity and vibrancy; secondly because the study of comics, like many of the studied comics themselves, mostly exist and take place today somewhere in the spectrum of digital environments, and thirdly because comics studies as a field operates within academic institutions and cultures, and therefore plays a role within established hierarchies of knowledge production.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.021 | 0.020 |
| Scholarly communication | 0.045 | 0.037 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.010 | 0.017 |
| Insufficient payload (model declined to judge) | 0.035 | 0.006 |
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".