Cultivating Comic Collections: An Exploration of the Information Worlds of Comic Librarians in Toronto
Bibliographic record
Abstract
This thesis explores the information worlds of librarians in Toronto who work with comics andgraphic novels across academic, school, and special libraries to characterize experiences with comics in work and personal leisure contexts and highlight comics librarianship as a specialty. The research design is informed by exploratory, ethnographic, and arts-based methodologies. Eleven librarians participated in a questionnaire, semi-structured interview, and information world mapping activities. Analysis involved a quantitative assessment of graphic representations and qualitative thematic analysis. This project illuminates informational dimensions of comics librarianship by investigating graphic representations of comic-information worlds, articulating information needs, identifying information sources, and examining work-related information behaviours. Comics librarianship is more than just creating collections; it is an act of cultivation through which librarians develop specialized knowledge, create diverse collections that represent their user communities, advocate for the value of visual materials, and draw upon their personal experiences with the medium.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.028 | 0.020 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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".