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Expert consensus methods in the humanities: An exploration of their potential
Rulkens,Charlotte C.S. Peels,Rik Mokkink,Lidwine B. Haven,Tamarinde Bouter,Lex
Rulkens,Charlotte C.S.
Peels,Rik
Mokkink,Lidwine B.
Haven,Tamarinde
Bouter,Lex
Abstract
Background Expert consensus methods are regularly used in natural, social, and life sciences. This article explores the potential of applying these methods more frequently in humanities research. Methods The authors reviewed literature and applied the philosophical methods of conceptual analysis and conceptual engineering. Results This article identifies and describes six main elements of expert consensus methods. It also provides an overview of the different types of expert consensus methods regularly used in the natural, social, and life sciences: Delphi studies, nominal groups, consensus conferences, and Glaser’s state of the art method. Subsequently, each of these types is illustrated by an example from the sciences. The article also presents the potential of and objections to the application of expert consensus methods there. It gives four examples of expert consensus methods that were applied in humanities research, also presented in line with the six elements. Conclusions The comparisons and categorization show that, as in the natural, social, and life sciences, expert consensus methods in the humanities can in some instances potentially accelerate the epistemic process and enhance transparency, replicability, diversity, and fair processes. Nevertheless, expert consensus methods need to be fine-tuned to do justice to the unique nature and approaches of the humanities and therefore further research is needed.
Description
Date
2024-12
Journal Title
Journal ISSN
Volume Title
Publisher
Research Projects
Organizational Units
Journal Issue
Keywords
Humanities, Consensus, Humans, Delphi Technique
Citation
Rulkens, C C S, Peels, R, Mokkink, L B, Haven, T & Bouter, L 2024, 'Expert consensus methods in the humanities : An exploration of their potential', F1000Research, vol. 13, 710. https://doi.org/10.12688/f1000research.148726.2
License
info:eu-repo/semantics/openAccess