Bibliographic record
Abstract
Michigan State University (MSU) is home to one of the largest library comics collections in North America, holding over three hundred thousand print comic book titles and artifacts. Inspired by the interdisciplinary opportunity offered by digital humanities practice, a research collaborative linked to the MSU Library Digital Scholarship Lab (DSL) developed a Collections as Data project focused on the Comic Art Collection. This team extracted and cleaned over forty-five thousand MARC records describing comics published in Canada, Mexico, and the United States. The dataset is openly available through a GitLab repository, where the team has shared data visualizations so that scholars and members of the public can explore and interrogate this unique collection. In order to bridge digital humanities with the popular culture legacy ofthe institution, the MSU comics community turned to bibliographic metadata as a new way to leverage the collection for scholarly analysis. In October 2020, the Department of English Graphic Possibilities Research Workshop gathered a group of scholars, librarians, Wikidatians, and enthusiasts for a virtual Wikidata edit-a-thon. This project report will present this event as a case study to discuss how linked open metadata may be used to create knowledge and how community knowledge can, in turn, enrich metadata. We explore not only how our participants utilized the open-access tool Mix’n’match to connect the Comic Art Collection dataset to Wikidata and increase awareness of lesser-known authors and regional publishers missing from OCLC and Library of Congress databases, but how the knowledge of this community in turn revealed issues of authority control.
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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.008 | 0.020 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.095 | 0.013 |
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".