Making visible the invisible through the analysis of acknowledgements in the humanities
Not on the map. Why?
Abstract (arXiv)
Purpose: Science is subject to a normative structure that includes how the contributions and interactions between scientists are rewarded. Authorship and citations have been the key elements within the reward system of science, whereas acknowledgements, despite being a well-established element in scholarly communication, have not received the same attention. This paper aims to put forward the bearing of acknowledgements in the humanities to bring to the foreground contributions and interactions that, otherwise, would remain invisible through traditional indicators of research performance. Design/methodology/approach: The study provides a comprehensive framework to understanding acknowledgements as part of the reward system with a special focus on its value in the humanities as a reflection of intellectual indebtedness. The distinctive features of research in the humanities are outlined and the role of acknowledgements as a source of contributorship information is reviewed to support these assumptions. Findings: Peer interactive communication is the prevailing support thanked in the acknowledgements of humanities, so the notion of acknowledgements as super-citations can make special sense in this area. Since single-authored papers still predominate as publishing pattern in this domain, the study of acknowledgements might help to understand social interactions and intellectual influences that lie behind a piece of research and are not visible through authorship. Originality/value: Previous works have proposed and explored the prevailing acknowledgement types by domain. This paper focuses on the humanities to show the role of acknowledgements within the reward system and highlight publication patterns and inherent research features which make acknowledgements particularly interesting in the area as reflection of the socio-cognitive structure of research.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Jun 2017.
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Citing papers per year: 2022: 2; 2023: 1.
3 papers.
- 2022Evaluation of Embedding Models for Automatic Extraction and Classification of Acknowledged Entities in Scientific Documentscited by 3
- 2022A Comprehensive Analysis of Acknowledgement Texts in Web of Science: a case study on four scientific domainscited by 1
- 2023Embedding Models for Supervised Automatic Extraction and Classification of Named Entities in Scientific Acknowledgementscited by 0
- 2023Embedding Models for Supervised Automatic Extraction and Classification of Named Entities in Scientific Acknowledgements25 Jul 2023
- 2022A Comprehensive Analysis of Acknowledgement Texts in Web of Science: a case study on four scientific domains18 Oct 2022
- 2022Evaluation of Embedding Models for Automatic Extraction and Classification of Acknowledged Entities in Scientific Documents22 Jun 2022