Retrieving and using visual resources: Challenges and opportunities for research and education
Bibliographic record
Abstract
Abstract Visual resources are used in a variety of settings for many different purposes. Technological advances facilitate numerous applications of digital images and other visual materials in work and leisure, resulting in increasing availability of and demand for such resources. A major challenge for information professionals is to organize digital visual resources effectively to meet the needs of users with different backgrounds and interests. For example, how do we provide access to the content of such resources and design an information system for the general public as well as subject specialists? A related challenge is the education of visual resource professionals because their roles and responsibilities have expanded in the digital era (Iyer, ). What knowledge and skills should visual resource professionals of the 21st century possess? How do we prepare them to facilitate the retrieval and use of digital visual resources and manage such resources for short‐term and long‐term access? The proposed program is designed to facilitate a dialog among practitioners, educators, and a panel of researchers with experience investigating the retrieval and use of visual resources. To provide a context for the dialog, panelists will use the first half of the program to highlight what they have learned from their research and teaching. These brief presentations will be followed by a discussion between the audience and the panelists. The audience will be encouraged to share their views on the following topics and any additional topics of great interest to them: How do users search for visual resources in the absence of information systems? How do users find their access to visual resources supported or inhibited by the information systems put in place for them? What are the opportunities for practitioners, subject specialists, researchers, and educators to collaborate and provide learning experiences with visual resources for LIS students? What competencies are needed by information professionals in order to build and sustain good visual resource systems? The program will be of interest to information science educators, specialists in digital asset management, and information professionals who work with visual resources (art and special collections librarians, digital librarians, archivists and museum curators).
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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 | 0.030 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.023 | 0.027 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".