A Survey of Graphic Novel Collection and Use in American Public Libraries
Bibliographic record
Abstract
Abstract Objective – The objective of this study was to survey American public libraries about their collection and use of graphic novels and compare their use to similar data collected about video games. Methods – Public libraries were identified and contacted electronically for participation through an open US government database of public library systems. The libraries contacted were asked to participate voluntarily. Results – The results indicated that both graphic novels and video games have become a common part of library collections, and both media can have high levels of impact on circulation. Results indicated that while almost all libraries surveyed had some graphic novels in their collections, those serving larger populations were much more likely to use graphic novels in patron outreach. Similarly, video game collection was also more commonly found in libraries serving larger populations. Results also showed that young readers were the primary users of graphic novels. Conclusion – Responses provided a clear indicator that graphic novels are a near-ubiquitous part of public libraries today. The results on readership bolster the concept of graphic novels as a gateway to adult literacy. The results also highlight differences between larger and smaller libraries in terms of resource allocations towards new media. The patron demographics associated with comics show that library cooperation could be a potential marketing tool for comic book companies.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".