The operationalization of “fields” as WoS subject categories ( WC s) in evaluative bibliometrics: The cases of “library and information science” and “science & technology studies”
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Normalization of citation scores using reference sets based on W eb of S cience subject categories ( WC s) has become an established (“best”) practice in evaluative bibliometrics. For example, the T imes H igher E ducation W orld U niversity R ankings are, among other things, based on this operationalization. However, WC s were developed decades ago for the purpose of information retrieval and evolved incrementally with the database; the classification is machine‐based and partially manually corrected. Using the WC “information science & library science” and the WC s attributed to journals in the field of “science and technology studies,” we show that WC s do not provide sufficient analytical clarity to carry bibliometric normalization in evaluation practices because of “indexer effects.” Can the compliance with “best practices” be replaced with an ambition to develop “best possible practices”? New research questions can then be envisaged.
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Different bibliographic DBs sort their indexed content based on their own individual subject classification schemes.
“different bibliographic DBs sort their indexed content based on their own individual subject classification schemes”
From Web of Science (WoS) and Scopus: The Titans of Bibliographic Information in Today’s Academic World (2021) · cites this paper as [176]
Prior attempts to describe a normalized citation metric have resulted in imperfect systems for the comparison of diverse scholarly works, either because they measure only the average performance of a group of papers or because the article of interest is measured against a control group that includes widely varying areas of science.
“prior attempts to describe a normalized citation metric have resulted in imperfect systems for the comparison of diverse scholarly works (S1 Text and Fig 4E), either because they measure only the average performance of a group of papers [62] or because the article of interest is measured against a control group that includes widely varying areas of science [17,32,48].”
From Relative Citation Ratio (RCR): A New Metric That Uses Citation Rates to Measure Influence at the Article Level (2016) · cites this paper as [48]
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- Authors
- Leydesdorff, L., & Bornmann, L.
- Affiliations
- Amsterdam School of Communication Research (ASCoR) University of Amsterdam P.O. Box 15793, NL‐1001 NG Amsterdam The Netherlands
- Max Planck Society (DE)
- ORCID
- Bornmann, Lutz
- Venue
- Journal of the Association for Information Science and Technology
- Publisher
- Wiley
- Published
- 2015-01-08
- Volume
- 67
- Issue
- 3
- Pages
- 707-714
- ISSN
- 2330-1635 (ISSN-L)
- 2330-1643
- OpenAlex
- W2591659376
- Cited by
- 129
- References
- 71
- Citations by year
- 2026: 3 · 2025: 9 · 2024: 9 · 2023: 6 · 2022: 10 · 2021: 13 · 2020: 14 · 2019: 10 · 2018: 16 · 2017: 11 · 2016: 17 · 2015: 7 · 2014: 4
- FWCI
- 15.51
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Journal of the Association for Information Science and Technology (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- UvA-DARE (University of Amsterdam) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › scientometrics and bibliometrics research
- Physical Sciences › Computer Science › Information Systems › Web visibility and informetrics
- Physical Sciences › Computer Science › Information Systems › Library Science and Information
- Keywords
- library and information science, science and technology studies, journal classification, Times Higher Education World University Rankings, bibliometric analysis, best practices
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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