Revisiting Instructional Approaches in Response to Emerging Cataloging Standards
Bibliographic record
Abstract
The ever-shifting landscape of cataloging standards over the last decade has kept library and information science (LIS) educators on their toes, and continuing developments only promise to maintain this trend. Examples include the publication of the International Federation of Library Association & Institution’s (IFLA’s) conceptual model Library Reference Model (LRM) in 2017, the release of the new and heavily revised version of the cataloging content standard Resource Description and Access (RDA) in 2020, and Library of Congress’ upcoming Bibliographic Framework (BIBFRAME) standard for encoding and publishing library data. These new standards have altered the way in which cataloging work is conceived and discussed, radically changed the interfaces used for accessing cataloging documentation, and are now spurring the creation of new software and tools for cataloging work, including Library of Congress’ new Marva metadata editor. At the same time, the increasing inclusion of linked data projects in libraries, archives, and other cultural heritage institutions are requiring new skills, practices, and workflows that cataloging and metadata librarians must be prepared for. With many of these standards and initiatives not fully implemented in the majority of libraries, cataloging educators face a dilemma in deciding how best to prepare their students to successfully navigate this time of change, where old and new standards and practices intersect. The Technical Services Education SIG session will include a panel of three educators (two of whom are current cataloging practitioners) with unique perspectives on strategies for teaching toward emerging cataloging standards. After brief presentations by each of the panelists, audience members will be encouraged to ask questions and offer their own experiences and ideas concerning this area of LIS education. This conversation aligns well with the ALISE conference theme of “Go Back and Get It: From One Narrative to Many” as cataloging practice and education must be in constant dialogue about the preparation of LIS students for the lifespan of their careers. How can LIS educators prepare students for an environment in which different institutions are facing drastically different plans and timelines for the implementation of new standards? How can teaching practices be adjusted to best leverage current best practices alongside new strategies? And how can study of past, present, and emerging cataloging standards and practices provide a solid foundation on which LIS students can build throughout their careers? This panel will offer opportunities for LIS educators to reach back to knowledge and experiences concerning previous standards transitions, share best practices for addressing the current, dynamic environment, and look toward the future of cataloging and metadata education and preparation.
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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 | 0.052 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.020 | 0.018 |
| Open science | 0.007 | 0.015 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".