Information organization in libraries, archives and museums: Converging practices and collaboration opportunities
Bibliographic record
Abstract
Abstract As cultural institutions libraries, archives, and museums (LAMs) share the mission to organize information objects, artifacts, and data for user access and enlightenment. While (LAMs) may follow different metadata standards and procedures to manage their collections and each type of institution has unique information organization and service concerns, digital technologies have enabled them to create, organize, preserve, and provide access to digital collections for global audience. Increasingly LAMs are converging in their information organization and management effort (LAM entries in Hangingtogether.org ; Zorich, Waibel & Erway ), and the cultural silos created by libraries, archives, and museums are being integrated or rendered transparent for users (Calhoun 2006; Christenson and Tennant 2005; Uzwyshyn 2007). The proposed panel is designed to examine the convergence of information organization practices of libraries, archives, and museums; explore collaboration opportunities; and discuss the implications of LAM information organization practices for educating information professionals for these cultural heritage institutions. The panel consists of five speakers who will cover (1) the use of a faceted classification to organize museum artifacts and support website development; (2) metadata design and applications for organizing and preserving information objects for several types of cultural institutions; (3) the development of the Biodiversity Heritage Library and the involvement of libraries and non‐library specialists in this effort; (4) analysis of descriptive standards used by cultural organizations and areas where libraries, archives, and museums can collaborate; and (5) collaboration among cultural institutions, especially in the technology area.
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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 | 0.048 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.016 | 0.024 |
| Scholarly communication | 0.034 | 0.024 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".