Bibliographic record
Abstract
While the major uses of the catalogue continue to be its search and inventory functions, the move from card catalogue to computer has opened up new possibilities for the catalogue to create impact as a data source. This paper explores the potential of library metadata as a source for scholarly research. As Calhoun (2014) points out, machine-to-machine web services have allowed developers of websites to reuse data from booksellers and Google Books on their own sites. Theorists and practitioners have long called for improvements in library metadata to allow for better sharing not only within the library community but more widely (cf. Bannerjee, 2002; Coyle, 2008), resulting in international projects presenting catalogue data in XML and / or RDF formats (cf. Library of Congress, 2012; IFLA, 2013; Willer and Dunsire, 2013). For individual users, Library Management Systems and discovery layers offer a variety of download options, usually including text, CSV and RIS files. Many libraries also allow their holdings to be searched and downloaded via reference management software, (Thomson Reuters, 2014). Despite calls for greater output options (Calhoun, 2006), this is an area about which little has been published. The availability of a plethora of download options indicates that libraries recognize that users have a wide range of needs. However, many web offerings are still targeted solely at reference management. This paper explores the uses to which catalogue data can be put by researchers involved in digital scholarship (cf. Tomm, 2012) and by libraries themselves in evaluating their collections (cf. OCLC, 2014). Quantitative Digital Humanities tools offer possibilities for further exploration of library’s holdings and for catalogue information to be utilized as Big Data. Metadata output for scholarly research is an emergent field in which libraries can increase their impact with small amendments to their current systems and web offerings. References Banerjee, Kyle (2002). How Does XML Help Libraries? Computers in Libraries 22(8), Calhoun, Karen (2006). The Changing Nature of the Catalog and Its Integration with Other Discovery Tools. Washington DC: Library of Congress, < http://www.loc.gov/catdir/calhoun-report-final.pdf> Calhoun, Karen (2014). Exploring Digital Libraries: Foundations, Practice, Prospects. London: Facet. Coyle, Karen (2008). Keynote. R&D: RDA in RDF , or, Can Resource Description Become Rigorous Data? Code4Lib Conference, March, IFLA Cataloguing Section ISBD/XML Review Group (2013). Activity Report, 2012-2013. IFLA, 2013, < http://www.ifla.org/files/assets/cataloguing/isbdrg/isbd-xml-activities_2012-2013.pdf> Library of Congress (2012). Bibliographic Framework as a Web of Data: Linked Model and Supporting Services. Library of Congress, < http://www.ifla.org/files/assets/cataloguing/isbdrg/isbd-xml-activities_2012-2013.pdf> OCLC (2014). Collection Evaluation, < http://www.oclc.org/collection-evaluation.en.html> Thomson Reuters (2014). Endnote. Online Databases. Endnote Export Providers, < http://endnote.com/en/online-databases> Tomm, Jillian (2012). The Imprint of the Scholar: an Analysis of the Printed Books of McGill University’s Raymond Klobansky Collection: thesis. Montreal: McGill University, < http://digitool.library.mcgill.ca/R/-?func=dbin-jump-full¤t_base=GEN01&object_id=114196> Willer, Mirna and Dunsire, Gordon (2013). Bibliographic Information Organization in the Semantic Web. Oxford: Chandos.
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 | 0.047 | 0.155 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.032 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.041 | 0.023 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.008 |
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".