Bibliographic record
Abstract
Throughout the twentieth and twenty-first centuries, critics have continually reconsidered the compatibility of computation and the humanities. Often, questions of “form” have played key roles in these conversations. In the 1940s, for example, critics asked whether word-counting might capture “formal units” of poetry like style. By the 2010s, scholars debated with new fervor whether computational methods could—or should—be used to track aesthetic structures like narrative, character, or genre, and especially without eliding rich socio-historical contexts. Today, these debates are by no means over. But they look different after a half decade of new work, and a cascade of theoretical and technical developments—perhaps most prominently, increasing attention to audiovisual materials and the explosion of transformer-based generative AI. In this special issue, we bring together scholars from across multiple disciplines to reconsider the intersections between computation and form for this emerging technological and critical moment. Together, their work represents a digital humanities in multiple types of transition. The essays collected in this issue refine existing computational critical methods to enable more nuanced and contextualized formal analysis; they apply these methods beyond literary or aesthetic canons to broader ranges of audiovisual, pop-cultural, and technical artifacts, from comics and conspiracy theories to viral TikToks; and they consider prompt-based LLMs, not only as new tools of aesthetic analysis, but also as cultural and technical “forms” in their own right. The articles in this special issue will be published in groups on a rolling basis over the coming weeks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.078 |
| Scholarly communication | 0.018 | 0.025 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| 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".