State of the art/science: Visual methods and information behavior research
Bibliographic record
Abstract
Abstract This panel reports on methodological innovation now underway as information behavior scholars begin to experiment with visual methods. The session launches with a succinct introduction to visual methods by Jenna Hartel and then showcases three exemplar visual research designs. First, Dianne Sonnenwald presents the “information horizon interview” (, ), the singular visual method native to the information behavior community. Second, Anna Lundh () describes her techniques for capturing and analyzing primary school children's information activities utilizing video recordings. Third, Nancy Fried Foster (Foster & Gibbons, ) reports how students, staff and faculty members produce maps, drawings, and photographs as a means of contributing their specialist knowledge to the design of library technologies and spaces at the University of Rochester. Altogether, the panel will present a collage of innovative visual research designs and engage the associated epistemological, theoretical, methodological, and empirical issues. All speakers will have 15 minutes and be timed to allow a minimum of 30 minutes for audience questions, comments, and discussion. Upon the conclusion attendees will have gained: knowledge of the state of the art/science of visual methods in information behavior research; an appreciation for the richness the approach brings to the specialty; and a platform to take new visual research designs forward.
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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 | 0.143 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.004 | 0.032 |
| Scholarly communication | 0.028 | 0.015 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".