Comparing Disciplinary Classifications in SSH: Organizational, Channel-Based, and Text-Based Perspectives

TL;DR

This paper compares organizational, channel-based, and text-based disciplinary classifications in the SSH, showing that text-based methods align closely with channel categories and offer valuable insights into disciplinary profiles and dynamics.

Contribution

It introduces a comparative analysis of classification systems in SSH, highlighting the effectiveness of text-based methods in capturing disciplinary nuances and validating classifications.

Findings

Text-based classification aligns more with channel-based categories.

Text features better capture author affiliations than channels.

Multidisciplinary journals show distinctive disciplinary profiles.

Abstract

This study investigates how different approaches to disciplinary classification represent the Social Sciences and Humanities (SSH) in the Flemish VABB-SHW database. We compare organizational classification (based on author affiliation), channel-based cognitive classification (based on publication venues), and text-based publication-level classification (using channel titles, publication titles, and abstracts, depending on availability). The analysis shows that text-based classification generally aligns more closely with channel-based categories, confirming that the channel choice provides relevant information about publication content. At the same time, it is closer to organizational classification than channel-based categories are, suggesting that textual features capture author affiliations more directly than publishing channels do. Comparison across the three systems highlights cases of convergence and divergence, offering insights into how disciplines such as "Sociology" and "History" extend across fields, while "Law" remains more contained. Publication-level classification also clarifies the disciplinary profiles of multidisciplinary journals in the database, which in VABB-SHW show distinctive profiles with stronger emphases on SSH and health sciences. At the journal level, fewer than half of outlets with more than 50 publications have their channel-level classification fully or partially supported by more than 90% of publications. These results demonstrate the added value of text-based methods for validating classifications and for analysing disciplinary dynamics.