Bibliographic record
Abstract
While working as production assistants for the National Network of Equitable Library Service (NNELS), an organization that creates and shares accessible versions of books to people with print disabilities, we were tasked with a challenging request from a user: Could we make an accessible version of the comic book The Walking Dead? Audio description services are available to the visually impaired in a few different venues such as television, movies, and live theatre. Guidelines for the creation of these descriptive texts are available to potential creators, but in our case, we could find nothing that would help guide us to create a described comic book. While some people and organizations have created prose novelizations of comic books, these simply tell the story, and do not include the unique visual aspects of reading a comic book. We have found that it is possible to create a balanced description that combines the visual grammar of a comic with the narrative story. In addition to creating a described comic book, we are developing guiding documentation that will be a necessary tool to ensure that visually impaired readers have a comic book experience (CBE) that (a) closely matches the CBE of a sighted reader, and (b) is standardized across producers, so that the onus of understanding the approach to comic book description (CBD) is not put on the visually impaired reader. At this point in our work, we need more feedback from users with print disabilities to ensure we are meeting the highest standards.
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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.018 |
| Scholarly communication | 0.022 | 0.022 |
| Open science | 0.003 | 0.037 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.047 | 0.017 |
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".