ArticleHeliyon2023
Clustering energy support beliefs to reveal unique sub-populations using self-organizing maps.
Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed, 4 citations in OpenAlex.
No citing paper in PubMed yet.
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Americans' support for energy sources is quite complex and dependent on a range of socio-demographic characteristics. In a novel approach to investigate energy opinions on renewables and fossil fuels, self-organizing maps are employed to cluster individuals solely on their energy beliefs using social survey data collected from Pew Research in the Spring of 2021. These energy preference clusters are then used in regression models to examine attitudes regarding energy policy in the United States. Results from the self-organizing map (SOM) analysis reveal four distinct clusters: energy traditionalists who oppose renewable sources due to partisan ideologies; energy renewers who strongly prefer investment in only renewable energy sources; energy universalists who universally support all forms of energy; and the aberrant cluster, individuals who prefer solar power greatly over wind energy but demonstrate no other energy preference patterns. Results from regression analyses reveal that SOM clusters are highly predictive of attitudes regarding energy policy. Taken together, these results reveal the unique capability of machine learning to categorize human attitudes - which should be of particular interest to energy policymakers when considering the opinions of the electorate.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.