Bibliographic record
Abstract
Developing collections of graphic novels and other works of narrative and sequential art has been making waves for the past few years, but libraries in France and Quebec have been \ncollecting “bande dessinée” for decades. As opposed to comic books, the Franco-Belgian format of choice is the “album”. These hard-covered books of about 50 pages are more than suitable for our institutions. Some collections in Quebec even top the 10,000 document mark! \n \nWhen the Bibliothèque Nationale du Québec merged with the City of Montréal’s central library, it was quite natural to develop the english-language Graphic Novel collection. \nUnfortunately, collections development in this area is still a burgeoning field. That is why Olivier Charbonneau, Subject Librarian and Researcher at Concordia University, was brought in to propose 1000 English language Graphic Novels for the collection. \n \nDifferent techniques were employed to select the documents. This lecture presents the methodology used to prepare the list and, more generally, insight about selecting English language Graphic Novels or book length comic books on an ongoing basis. Furthermore, the five areas of the collection will be discussed, namely: classic superhero, contemporary superhero, comic strips, underground and manga publications.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.108 | 0.008 |
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".