Partisans and the Use of Knowledge versus Science.

Ber Wiss

Hans Rausing Lecturer in History and Philosophy of Science, University of Cambridge, UK.

Published: September 2019

This paper explores the kind of knowledge that partisans profess in order to contribute to our studies of what has usually been thought of as the "denial of science." Building on the research of Robert Proctor, Naomi Oreskes and Erik Conway, I show that the tobacco interests and climate science skeptics usually described as "doubt mongers" also purveyed forms of certainty and rested their arguments on three different registers of truth: that of narrowly defined "facts" that could sustain a controversy, ideological commitments to free enterprise, and the truths of self-conscious partisans engaged in battle. Thus, in many respects they have used elements of general knowledge, as well as social, economic and political commitments, to argue against specific scientific findings. Further, at least in the case of climate skeptics, this denial has been in the service of an image of the nature of science and its proper relation to politics. Analyzing significant dichotomies in debates that cross the terrains of science and politics, and knowledge and science, I will argue that a clear articulation of the relations amongst them will be critical to our work to understand the character of climate science denial, but also of the climate sciences themselves.

Download full-text PDF

Source
http://dx.doi.org/10.1002/bewi.201900012DOI Listing

Publication Analysis

Top Keywords

climate science
8
science
6
partisans knowledge
4
knowledge versus
4
versus science
4
science paper
4
paper explores
4
explores kind
4
kind knowledge
4
knowledge partisans
4

Similar Publications

European agrifood and forestry education for a sustainable future - Gap analysis from an informatics approach.

Open Res Eur

October 2024

Department of Process and Life Science Engineering, Division of Food and Pharma, LTH, Faculty of Engineering, Lund University, Lund, Skåne County, SE-221 00, Sweden.

Background: The NextFood Project ( www.nextfood-project.eu) started work in 2018 to identify 'Categories of Skills' that students should be equipped with to address the upcoming global challenges within agrifood and forestry disciplines, and involved concepts such as sustainability, technological adaptation and networking.

View Article and Find Full Text PDF

The emergence of East Asian spring ephemerals and the unique ecosystem can be attributed primarily to vicariance, brought about by the Quaternary rifting of the Okinawa Trough, the formation of the East China Sea, and the isolation of the island chains of Ryukyu, Japan, and Taiwan from the Asian continent. The northern forests of Japan, dominated by and the associated , present a captivating display of spring-flowering ephemerals, including , , , and . Among these, is also considered part of the spring ephemerals.

View Article and Find Full Text PDF

Purpose: A promising feature of marine sponges is the potential anticancer efficacy of their secondary metabolites. The objective of this study was to explore the anticancer activities of compounds from the fungal symbiont of on breast cancer cells.

Methods: In the present research, , an endophytic fungal strain derived from the marine sponge was successfully isolated and characterized.

View Article and Find Full Text PDF

Intestinal infections affect approximately 450 million people globally, predominantly impacting children and immunocompromised individuals in low- and middle-income countries (LMICs) due to inadequate water, sanitation, and hygiene (WASH) conditions, poverty, malnutrition, and low literacy. In Kenya, the prevalence of intestinal infections is elevated by warm tropical climates and socioeconomic factors. This scoping review evaluates the national prevalence, risk factors, and contamination sources of intestinal protozoa in Kenya, using a One Health approach to synthesize existing data from various human, animal, and environmental studies.

View Article and Find Full Text PDF

This study evaluates three Machine Learning (ML) models-Temporal Kolmogorov-Arnold Networks (TKAN), Long Short-Term Memory (LSTM), and Temporal Convolutional Networks (TCN)-focusing on their capabilities to improve prediction accuracy and efficiency in streamflow forecasting. We adopt a data-centric approach, utilizing large, validated datasets to train the models, and apply SHapley Additive exPlanations (SHAP) to enhance the interpretability and reliability of the ML models. The results show that TKAN outperforms LSTM but slightly lags behind TCN in streamflow forecasting.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!