Lectura crítica de Oliver, C. (2021). "Introducing RDA: a guide to the basics after 3R" : 2da. ed. Chicago: ALA Editions. 200 pág. ISBN 978-0-8389-1908-8
Bibliographic record
Abstract
Esta segunda edición de Introducing RDA fue publicada por ALA Editions, editorial de American Library Association en 2021. Su autora, Christine Oliver, es jefa de metadatos y procesos de la University of Ottawa Library, miembro de la Canadian Federation of Library Associations y de la junta de gobierno de RDA (RDA Board). A once años de la primera edición de esta obra clásica de la bibliografía especializada en el estándar de catalogación RDA (Recursos, Descripción y Acceso), se han producido muchos cambios tanto en el modelo teórico que lo sustenta, como en el estándar mismo y en RDA Toolkit. Dichas transformaciones se materializan en RDA Toolkit a través del 3R Project (RDA Toolkit Restructure and Redesign Project) que se inicia en 2017 y culmina con la activación de la versión oficial de RDA Toolkit, totalmente alineada con el modelo teórico unificado IFLA LRM (Library Reference Model), el 15 de diciembre de 2020. 3R Project representa un punto de inflexión para RDA Toolkit, ya que introduce cambios realmente significativos respecto a la versión original que se encontraba disponible desde 2010. Presenta un diseño totalmente diferente, con una nueva organización, con cambios en el vocabulario y nuevas funcionalidades.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.071 | 0.075 |
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".