Personal and Professional Information Behaviours of Comic and Graphic Novel Librarians in Toronto
Bibliographic record
Abstract
This poster presents findings from a thesis study characterizing the information needs, sources, and behaviours of librarians in the Greater Toronto Area who work with comic and graphic novel collections across academic, school, and special library contexts. This project extends Sonnenwald’s information horizon interview (IHI) methodology, considering how work and leisure contexts influence information behaviours. This poster describes data collection with 11 participants; outlines findings from thematic analysis that identify common information needs and sources; discusses information behaviours within the frame of Hektor’s information activities; and showcases information world maps created by participants. Comprendre le partage d'information des chercheurs-artistes : Thèmes émergents RésuméCette affiche décrit la conception de la recherche et identifie les thèmes émergents d'une étude de thèse en cours, nommée Comprendre le partage d'information des chercheurs-artistes. L'étude doctorale fournira un portrait et une analyse de comment les chercheurs-artistes partagent (ou ne partagent pas) l'information entre eux en explorant les conditions qui encouragent et découragent le partage. Cette étude considère ce que cela signifie de partager et d'être récipient de ce partage, et elle met l'accent sur les aspects moins connus du processus de recherche. Mots-clésPartage d'information; Chercheur artiste; Recherche création
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".