Scientometrics and Communication Theory: Towards Theoretically Informed Indicators

A theory of citations should not consider cited and/or citing agents as its sole subject of study. One is able to study also the dynamics in the networks of communications. While communicating agents (e.g., authors, laboratories, journals) can be made comparable in terms of their publication and citation counts, one would expect the communication networks not to be homogeneous. The latent structures of the network indicate different codifications that span a space of possible ’translations’. The various subdynamics can be hypothesized from an evolutionary perspective. Using the network of aggregated journal-journal citations in Science & Technology Studies as an empirical case, the operation of such subdynamics can be demonstrated. Policy implications and the consequences for a theory-driven type of scientometrics will be elaborated.

💡 Research Summary

The paper challenges the conventional scientometric approach that treats citations merely as counts of cited and citing agents, arguing that this perspective overlooks the richer dynamics embedded in communication networks themselves. While authors, laboratories, and journals can be compared on the basis of publication and citation tallies, the authors contend that the underlying network of scholarly communication is far from homogeneous. Its latent structures reflect distinct codifications—sets of shared meanings, norms, and methodological conventions—that span a multidimensional “translation space.” From an evolutionary standpoint, these codifications give rise to sub‑dynamics that can be hypothesized as processes of variation, selection, diffusion, and translation.

To make these ideas concrete, the authors analyze an aggregated journal‑journal citation network within the field of Science & Technology Studies (STS). Using standard network‑analytic techniques (density, centrality, clustering, and temporal snapshots), they first map the overall topology of the STS citation landscape. Subsequent community‑detection reveals two dominant axes of codification. The first axis, which the authors label “methodological coding,” groups journals that share quantitative, empirical, or statistical research designs. The second axis, “thematic coding,” clusters journals that converge on particular social‑technical topics such as innovation, risk, or policy. These two codification dimensions are not static; over the period examined, methodological coding dominates early years, whereas thematic coding expands dramatically in the mid‑2000s, shifting the network’s structural center.

Crucially, the paper interprets the interaction between these sub‑networks through the lens of communication theory’s concepts of “code” and “translation.” When a journal adopts or adapts a code from another sub‑network, it functions as a translator, creating new citation pathways and thereby reconfiguring the overall network. This translation mechanism is presented as the engine of evolutionary change: it enables the emergence of novel interdisciplinary fields, accelerates the diffusion of ideas, and can trigger structural phase transitions in the citation network.

The authors argue that traditional citation metrics—impact factor, h‑index, raw citation counts—are insufficient for capturing these complex dynamics. They propose a suite of theory‑informed indicators that reflect the network’s latent structure and sub‑dynamic activity: (1) growth rates of identified sub‑networks, (2) frequency of code‑translation events, (3) translation centrality (the extent to which a journal bridges distinct codifications), and (4) multidimensional codification participation scores. Such indicators would allow research policymakers to detect emerging fields early, allocate resources to foster cross‑disciplinary translation, and evaluate the health of the scholarly communication ecosystem beyond simple citation tallies.

In the concluding section, the paper calls for a paradigm shift in scientometrics—from a purely quantitative counting exercise to a hybrid quantitative‑qualitative framework grounded in communication theory. It suggests future work should (a) extend the analysis to other disciplinary domains for comparative validation, (b) develop micro‑level models of code translation (e.g., agent‑based simulations of author behavior), and (c) explore real‑time data streams (pre‑prints, social media mentions) to construct dynamic, policy‑relevant indicators. By integrating network topology, latent codifications, and evolutionary translation processes, the authors envision a more nuanced, theoretically robust scientometrics capable of informing both scholarly understanding and science policy.