How to interpret algorithmically constructed topical structures of scientific fields? A case study of citation-based mappings of the research specialty of invasion biology
| dc.contributor.author | Matthias, Held | |
| dc.contributor.author | Theresa, Velden | |
| dc.date.accessioned | 2023-02-08T14:17:54Z | |
| dc.date.available | 2023-02-08T14:17:54Z | |
| dc.date.issued | 2022-11-01 | |
| dc.description.abstract | Often, bibliometric mapping studies remain at a very abstract level when assessing the validity or accuracy of the generated maps. In this case study of citation-based mappings of a research specialty, we dig deeper into the topical structures generated by the chosen mapping approaches and examine their correspondence to a sociologically informed understanding of the research specialty in question. Starting from a lexically delineated bibliometric field data set, we create an internal map of invasion biology by clustering the direct citation network with the Leiden algorithm. We obtain a topic structure that seems largely ordered by the empirical objects studied (species and habitat). To complement this view, we generate an external map of invasion biology by projecting the field data set onto the global Centre for Science and Technology Studies (CWTS) field classification. To better understand the representation of invasion biology by this global map, we use a manually coded set of invasion biological publications and investigate their citation-based interlinking with the fields defined by the global field classification. Our analysis highlights the variety of types of topical relatedness and epistemic interdependency that citations can stand for. Unless we assume that invasion biology is unique in this regard, our analysis suggests that global algorithmic field classification approaches that use citation links indiscriminately may struggle to reconstruct research specialties. | en |
| dc.identifier.eissn | 2641-3337 | |
| dc.identifier.uri | https://depositonce.tu-berlin.de/handle/11303/18173 | |
| dc.identifier.uri | https://doi.org/10.14279/depositonce-16966 | |
| dc.language.iso | en | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject.ddc | 310 Sammlungen allgemeiner Statistiken | de |
| dc.subject.other | algorithmic classification | en |
| dc.subject.other | bibliometrics | en |
| dc.subject.other | direct citation | en |
| dc.subject.other | field delineation | en |
| dc.subject.other | field mapping | en |
| dc.subject.other | invasion biology | en |
| dc.title | How to interpret algorithmically constructed topical structures of scientific fields? A case study of citation-based mappings of the research specialty of invasion biology | |
| dc.type | Article | |
| dc.type.version | publishedVersion | |
| dcterms.bibliographicCitation.doi | 10.1162/qss_a_00194 | |
| dcterms.bibliographicCitation.issue | 3 | |
| dcterms.bibliographicCitation.journaltitle | Quantitative Science Studies | |
| dcterms.bibliographicCitation.originalpublishername | MIT Press | |
| dcterms.bibliographicCitation.originalpublisherplace | Cambridge | |
| dcterms.bibliographicCitation.pageend | 671 | |
| dcterms.bibliographicCitation.pagestart | 651 | |
| dcterms.bibliographicCitation.volume | 3 | |
| dcterms.rightsHolder.reference | Creative-Commons-Lizenz | |
| tub.accessrights.dnb | free | * |
| tub.affiliation | Fak. 1 Geistes- und Bildungswissenschaften::Inst. Philosophie-, Literatur-, Wissenschafts- und Technikgeschichte::FG Sozialwissenschaftliche Wissenschafts- und Technikforschung | |
| tub.publisher.universityorinstitution | Technische Universität Berlin |