Aims and scope

Scientometrics is an international, peer-reviewed, monthly journal that publishes original research on all quantitative aspects of the production, communication, and use of scientific and technological information.

    Scientometrics welcomes both theoretical and empirical studies.
    Research articles present new methods and insights.
    Scientometrics publishes invited reviews and book reviews.
    Valuable for research workers, research administrators, librarians, and documentalists in central scientific agencies, ministries, research institutes, and laboratories.
    Highly visible and cited across various areas of science and research.

Special Issue on Large Language Models for Scientometrics (LLM4SCIM)
Submission Date: 2026-01-31

The launch of the special issue Artificial Intelligence for Scientometrics (AI4SCIM) has received great attention from the broad scientometrics community, with nearly 150 submissions and across a wide range of interests in AI-empowered methodological development for scientometrics studies, communications and understandings of AI’s broad impacts in scientometrics, etc. While we have foreseen the deep enagegemnt of large language models (LLMs) with broad scientometrics tasks, as well as its revolutionary influence on transforming the working patterns and thinking paradigms of the community, submissions to the AI4SCIM special issue demonstrate a dominant use of LLMs, indicating the scientometrics community’s much more enhanced interactions with AI, particularly, LLMs. Impressive practices include utilising LLMs as an effective tool for entity extraction, topic identification, summarisation, etc., examining and comparing the performance of different large and small language models in specific scientometrics tasks (e.g., classification), and developing LLM-empowered methods for deep literature analysis.

These endeavours inspire us to launch the second special issue on the trail, highlighting LLMs for Scientometrics (LLM4SCIM). This special issue further urges the scientometrics community to leverage the great opportunities introduced by LLMs to enhance our understanding, utilisation, development, and regulation in this AI-driven revolution. We particularly highlight the following challenges:

These endeavours inspire us to launch the second special issue on the trail, highlighting LLMs for Scientometrics (LLM4SCIM). This special issue further urges the scientometrics community to leverage the great opportunities introduced by LLMs to enhance our understanding, utilisation, development, and regulation in this AI-driven revolution. We particularly highlight the following challenges:

DESIGN: How can we create a comprehensive research framework to incorporate LLMs for designing scientometric research?

COMMUNICATION: How can we responsibly integrate LLMs with human knowledge for conducting scientometric studies (e.g., LLM-aided academic writing and publishing in the context of scientometrics, and LLM-supported metrics research and evaluative research)?

TOOLS: How can we develop LLM-empowered analytical tools for broad scientometric studies (e.g., deep literature analysis using LLMs, and LLMs as part of new scientometric tools)?

This special issue calls on the scientometrics community to publish high-quality research on broad topics of LLMs for scientometrics, including fundamental theories, conceptual understandings, novel methodological developments, and practical applications that address urgent scientometric needs.

Topics

LLMs for research evaluation (including peer review/expert opinion and their evaluation, etc.)
LLMs for metrics development (including cognitive link analysis, citation context analysis, sentiment analysis, etc.)
LLMs for literature-based analysis (including entity extraction, knowledge graphs, full-text content analysis, etc.)
LLMs for scientific information retrieval and access (including academic GPTs, retrieval-augmented generation, etc.)
LLMs for patentometrics, technometrics, and altmetrics
LLM-empowered prediction models for scientometrics, e.g., predicting research trends, impact, quality, etc., and technological forecasting
LLMs for research communication, e.g., publishing, scientific writing, qualitative approaches (e.g., academic surveys), and data reporting and visualisation
LLMs as part of new scientometric tools
Ethics regarding LLMs for scientometrics, e.g., biases, transparency, and the impact of AI-generated content on scientometric research.

Special Issue on Framework for the Responsible use of Assessments and Metrics in Evaluation (FRAME)
Submission Date: 2026-01-31

Dear Contributor to the Special Track “Framework for the Responsible use of Assessments and Metrics in Evaluation (FRAME)”,

This is to inform you that the organisers of the Special Track FRAME at the 20th International Conference on Scientometrics and Informetrics (ISSI2025) and the Editors of the journal Scientometrics have agreed on the publication of extended versions of presented at this Specia Track in a dedicated issue of Scientometrics. The special issue will be edited by Cinzia Daraio, Wolfgang Glänzel, and Juan Gorraiz.

Invited authors of FRAME contributions as well as invited researchers who previously indicated their interest in contributing to the special track but could not manage to submit their manuscripts in time are asked to submit their manuscripts no later than 15 November 2025 to the Editorial Manager of the journal. The manuscript should be a substantially extended version of their contribution (at least 25% new material), if already published in the conference proceedings. Note that above-mentioned deadline is stringent. After this date, the special issue will be closed.

Authors are asked to organise their manuscript according to the guidelines for contributors as described on the journal’s homepage and in each print issue of the journal. Depending on the selected papers’ length, the issue will admit a selection of about 30 contributions. Therefore, submitted papers should ideally not exceed 15–20 pages (including tables and figures).