Bibliographic record
Abstract
Implementing FRBR in Libraries: Key Issues and Future DirectionsZhang, Yin and Athena Salaba. Implementing FRBR in Libraries: Key Issues and Future Directions. New York, NY: Neal-Schuman Publishers, Inc., 2009. Print. 154 pp. $80.00 USD. ISBN-13: 978-1-55570-661-6.Table of Contents and Preface available at (visited October 6, 2013).The information landscape of the twenty-first century is very different from that of the previous century. Conseguently, the library community has created theoretical models to explain the changes in users' information seeking behaviour. One of these theoretical models that attempts to explain users' interactions with bibliographic information contained within library catalogues is Functional Requirements for Bibliographic Records (FRBR) created by the International Federation of Library Associations and Institutions (IFLA) in 1998. Functional Reguirements for Bibliographic Records (FRBR) is an entity relationship model.In this book Zhang and Salaba discuss how libraries are applying the FRBR model. There are three objectives of the book Implementing FRBR in Libraries: Key Issues and Future Directions: (1) to provide an overview of the current status of FRBR development; (2) to identify the key issues that need to be addressed; and (3) to point to future directions of FRBR (Zhang and Salaba, p. x). The seven chapters in this book give the reader an insight into the impact of the FRBR model on current research, cataloguing standards and practices, and its application and implementation in libraries.The first chapter of this book gives an overview of the FRBR model by explaining what FRBR is, the reasons behind its introduction, the potential benefits of implementing FRBR and the challenges facing FRBR development.Chapter 2 describes the FRBR model in detail. This chapter discusses the entity groups that are a crucial part of the model as well as topics such as further development of the FRBR model and related models. The family of related models include Functional Reguirements for Authority Data (FRAD) and Functional Reguirements for Subject Authority Data (FRSAD). Interoperability with other models and critical issues and challenges in the FRBR model itself are also examined in this chapter.The third chapter reviews the impact of FRBR on current cataloguing standards and practices by answering the guestion: what changes will FRBR bring? The authors examine the impact of FRBR on international cataloguing principles; description standards such as the international standard bibliographic description, resource description and access; Dublin Core metadata initiative and other cataloguing standards; changes in encoding standards; and critical issues in cataloguing. …
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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 | 0.060 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.028 | 0.064 |
| Open science | 0.008 | 0.013 |
| Research integrity | 0.019 | 0.017 |
| Insufficient payload (model declined to judge) | 0.030 | 0.016 |
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".