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Meso-level retrieval: IR-bibliometrics interplay and hybrid citation-words methods in scientific fields delineation

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Abstract

In this position paper, we comment on various approaches to the delineation of scientific fields or domains, a typical prerequisite for a wide class of bibliometric studies. There is growing evidence that this meso-level, between micro targets of typical IR and large disciplines handled by macro-level bibliometric studies, takes full advantage of hybrid approaches. Firstly, delineation tasks gain to combine the a priori thinking of traditional IR, which typically involves clearly targeted expectations, and the a posteriori thinking of bibliometric mapping, where the decisions are built on external structuring of the domain in a wider context. The combination of the two ways of thought is far from new, with IR increasingly building on bibliometric networks for query expansion, and bibliometrics building on IR for evaluating and refining its outcomes. Secondly, delineation benefits from the multi-network perspective, which gives different representations of the scientific topics, usually all the more converging than the objects are dense and well separated. Focusing on two basic networks—words and citations—various sequences or combinations of operations are discussed. Bibliometrics and IR, especially when properly combined in multi-network approaches, provide an efficient toolbox for studies of domains delimitation. It should be recalled however that the context of such studies is often loaded with policy stakes that ask for cautious supervision and consultation processes.

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Notes

  1. Amongst Thomson-Reuters nomenclatures, the "subject categories" of SCI classification allow for overlaps mostly in terms of journals or journals sections.

  2. For a typology of IR models and the perspective of the "cognitive actor", see Ingwersen and Järvelin (2005).

  3. Assume articles B and A share the theoretical background and C and A share the domain of application. In bibliographic coupling articles B and C may both be attracted by A on quite different semantic aspects, while without epistemic relation. The argument is already found in Martyn (1964). Even mitigated by statistical aggregation, it expresses the cost associated to the statistical efficiency of bibliometric clustering. The use of hard-clustering, simple and fast, worsens this limitation. An overlapping technique might classify A, once with B, once with C. IR scholars warned against the holistic character of several mapping techniques, source of noise including for query expansion purposes.

  4. we are indebted to an anonymous referee for stressing this point.

  5. Even Latourian citations or negative citations do not add much noise to co-citation topics.

  6. High-precision is expected from a strategy focused on strong forms—heavy intersections—with check of the specific words/references of the native clusters c and w; high-recall is expected from a strategy based on the full content of c and w clusters with strong overlaps. Intermediary strategies can focus on intuitive groupings of areas along the diagonal sequence.

  7. for example by ruling out papers without a given number or proportion of specific references.

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Acknowledgments

The author thanks Alain Lelu, Université de Franche-Comté and Loria, Nancy, Elise Bassecoulard, formerly Inra-Lereco, and anonymous referees, for helpful remarks; Patricia Laurens and Antoine Schoen, ESIEE, Marne la Vallée, for permission to use the genomics map, from our previous co-work.

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  1. Lereco U1134, SAE2 Department, INRA, Rue de la Géraudière, BP71627, 44316, Nantes Cedex 03, France

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Zitt, M. Meso-level retrieval: IR-bibliometrics interplay and hybrid citation-words methods in scientific fields delineation. Scientometrics 102, 2223–2245 (2015). https://doi.org/10.1007/s11192-014-1482-5

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