Identifying and Capturing the Semantic Aspects of Citations
Open accessAbstract
This dissertation presents new work in understanding the nature of citations, their potential different purposes and how they are used. This research addresses a fundamental question: ‘Should all citations be treated equally?’ It explores the concept of influential citations and examines key features for their identification. It then investigates the key challenges in capturing these semantic aspects of citations and then presents solutions that overcome current limitations in the domain. The dissertation provides an overview of current bibliometrics that use raw citation counts and then details the problems associated with these methodologies. It then presents an overview of two areas in which bibliometrics and citation data are already being applied: information retrieval and research evaluation. The first study (Chapter 3) focuses on the second of these areas, the use of citation data in research evaluation; it is the largest investigation to date into the correlation between peer review and bibliometrics at the institutional / discipline level, using data from the UK’s Research Excellence Framework (REF2014). This study was the first to identify strong correlations between simple citation-based indicators and aggregate peer review results in approximately one third of domains covered by the REF2014 process, notably those domains in which the peer review panels used citation data to inform their peer review decisions. There is already wide-scale usage of bibliometrics and…
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Registry
Cites
Statistics
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Citations are inherently complex and heterogeneous, reflecting a wide range of motivations and rhetorical functions within scholarly writing.
“citations are inherently complex and heterogeneous: they reflect a wide range of motivations and rhetorical functions within scholarly writing (Pride, 2022; Tahamtan & Bornmann, 2019)”
From CiteFusion: an ensemble framework for citation intent classification harnessing dual-model binary couples and SHAP analyses (2025) · cites this paper as Pride, 2022
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Cited by 1
- Authors
- Pride, D.
- Corresponding author
- Pride, David
- ORCID
- Pride, David
- Venue
- The Open University
- Publisher
- The Open University
- Published
- 2022
- OpenAlex
- W6944957979
- Cited by
- 1
- References
- 0
- Citations by year
- 2025: 1
- Type
- dissertation
- Language
- en
- Open access
- green
- doi.org
- Locations
- Open Research Online (The Open University) (acceptedVersion)
- Open Research Online (The Open University)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › scientometrics and bibliometrics research
- Physical Sciences › Computer Science › Information Systems › Web visibility and informetrics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Psychology Research and Bibliometrics
- Keywords
- Research Excellence Framework, peer review, citation metrics, h-index, journal impact factor, citation annotation
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.
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