VII Encuentro de Catalogación y Metadatos 2012: memoria
Bibliographic record
Abstract
On March 31, 2013, the Library of Congress of the United States started the implementation of the new cataloging standard, RDA (Resource Description and Access). Also, another libraries around the world like the National Agricultural Library and the National Library of Medicine of the United States, the British Library, the Library and Archives of Canada, the German National Library and the National Library of Australia also began during that year the RDA implementation. Likewise, in Latin America, several libraries began planning and implementation work of RDA. In Mexico, the National Library of Mexico and the Library System of the National Autonomous University of Mexico began implementing RDA. Additionally, library schools began to include in their training programs teaching of this new cataloging standard. In addition to this, the application and use of technology in generating catalogs have begun to impact the shape and characteristics of them. The aforementioned prompted the need to bring together in the Seventh Meeting of Cataloging and Metadata all those librarians involved in the theory and practice of cataloging and metadata to discuss the problems and challenges of research, education and professional practice related to cataloging and metadata, in order to identify possible alternatives to address them successfully. The documents included in this publication will contribute to the increase the literature in this LIS area, thereby supporting the development of research, teaching and practice of information organization, cataloging and metadata in Latin American countries.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.046 | 0.051 |
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".