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The final dataset contained several isolated gaps in donations and organizational expenses. In such cases, the missing quantities were linearly interpolated from the preceding and following years.
We used the implementation of LDA in the Structural Topic Model (STM) package for R (Roberts et al. 2019). STM extends LDA by including document-level covariates that can affect the prevalence and content of topics. However, due to the size of the corpus and constraints on time and computing power, we did not include any document covariates in the estimation, in which case STM becomes identical to LDA. We have provided an explanation of LDA in the Supplemental Material.
On average, 8.3% of documents per year from organizations in the top quintile related to climate and energy, compared to 17% of documents from organizations in the middle quintile.
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Available upon request from the author for scholarly research.
This research was funded in part by a grant from the Institute at Brown for Environment and Society, Brown University, Providence RI.
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Brulle, R.J., Hall, G., Loy, L. et al. Obstructing action: foundation funding and US climate change counter-movement organizations. Climatic Change 166, 17 (2021). https://doi.org/10.1007/s10584-021-03117-w