| dc.description.abstract | Introduction
The rise of post-truth and the 2016 presidential election of Donald Trump posed a new threat to the scientific enterprise of the USA. Previous research focused on demarcating fact from non-fact to deal with the issue of loss of trust in science stemming from post-truth. I propose to analyze the demarcation efforts of scientists themselves, through the March for Science protests of 2017. This yields new insights on how the scientific community, the most affected by post-truth, deals with this complex issue.
Theory
To analyze the demarcation effort of the scientific community, I adopt a constructivist perspective on the authority and legitimacy of science as a knowledge practice. I use a boundary-work framework consisting of three interrelated components: the attribution of selected characteristics to science, the type of boundary-work employed, and the professional interests pursued.
Methodology
I collect my data from a combination of google search, an official livestream video and official photography galleries for the Marches for Science in Washington D.C., San Francisco, and Seattle. I collect images of signs, posters and banners from the three Marches and code the displayed slogans, statements and sentences according to the boundary-work framework.
Results
I find that science is characterized as objective, engaged and beneficial. These attributes are employed to expel post-truth and Trump from legitimate epistemology, and from controlling and influencing the federal scientific enterprise. I find that the attributions of engaged and beneficial are part of a novel demarcation strategy employed specifically against post-truth. There are some inconsistencies in the three attributes, hinting at possible weaknesses of the strategy.
Conclusion and discussion
The findings suggest that future strategies could benefit from adapting the attributes in order to eliminate contradictions and inconsistencies which could hinder the success of the boundary-work. In terms of limitations and generalizability, the presented research can benefit from a more exhaustive sampling with inclusion of more diverse sources of data, in combination with an extension of the sampled countries, in order to increase the generalizability. | |
