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Abstract In 2020, Arizonans approved Proposition 207, the Smart and Safe Arizona Act, which legalized recreational marijuana sales. Previous research has typically used non‐spatial survey data to understand marijuana legalization voting patterns. However, voting behavior can, in part, be shaped by geographic context, or place, which is unaccounted for in aspatial survey data. We use multiscale geographically weighted regression to analyze how place shaped Proposition 207 voting behavior, independently of demographic variations across space. We find significant spatial variability in the sensitivity of voting for Proposition 207 to changes in several of the predictor variables of opposition and support for recreational marijuana legalization. We argue that local statistical modeling approaches provide a more in‐depth understanding of ballot measure voting behavior than the current use of global models. Related ArticlesBranton, Regina, and Ronald J. McGauvran. 2018. “Mary Jane Rocks the Vote: The Impact of Climate Context on Support for Cannabis Initiatives.”Politics & Policy46(2): 209–32.https://doi.org/10.1111/polp.12248.Brekken, Katheryn C., and Vanessa M. Fenley. 2020. “Part of the Narrative: Generic News Frames in the U.S. Recreational Marijuana Policy Subsystem.”Politics & Policy49(1): 6–32.https://doi.org/10.1111/polp.12388.Fisk, Jonathan M., Joseph A. Vonasek, and Elvis Davis. 2018. “‘Pot'reneurial Politics: The Budgetary Highs and Lows of Recreational Marijuana Policy Innovation.”Politics & Policy46(2): 189–208.https://doi.org/10.1111/polp.12246.
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A Log‐Ratio‐Based Algorithm for Petrologic Mass‐Balance Problems and Uncertainty Assessment
Abstract We provide a new algorithm for mass‐balance calculations in petrology and geochemistry based on the log‐ratio approach championed initially by John Aitchison (e.g., Aitchison, 1982,https://doi.org/10.1111/j.2517-6161.1982.tb01195.x; Aitchison, 1984,https://doi.org/10.1007/bf01029316) along with the underlying principles, mathematical frameworks, and data requirements. Log‐ratio Inversion of Mixed End‐members (LIME) is written in MATLAB and calculates phase proportions in an experiment or rock given a bulk composition, the composition of each phase, and the associated compositional uncertainties. An important advantage of LIME is that performing the mass‐balance calculation in inverse log‐ratio space constrains phase proportions to be between 0 and 100 wt.%. Further, the resulting LIME phase proportions provide realistic estimates of uncertainty regardless of data distribution. These two characteristics of LIME improve upon standard multiple linear regression techniques, which may yield negative values for phase proportions if non‐constrained or report oversimplified symmetric errors. Primary applications of LIME include estimating phase abundances, calculating melting and metamorphic reaction stoichiometries, and checking for open system behavior in phase equilibria experiments. The technique presented here covers whole‐rock analysis, mineralogy, and phase abundance, but could be extended to isotopic tracers, trace element modeling, and regolith component un‐mixing. We highlight the importance of uncertainty estimations for phase abundances to the fields of petrology and geochemistry by comparing our results from LIME to previously published mass‐balance calculations. Furthermore, we present case studies that demonstrate the role of mass‐balance calculations in determining magma crystallinity and defining melting reactions.
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- Award ID(s):
- 2047960
- PAR ID:
- 10518555
- Publisher / Repository:
- AGU
- Date Published:
- Journal Name:
- Geochemistry, Geophysics, Geosystems
- Volume:
- 24
- Issue:
- 12
- ISSN:
- 1525-2027
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1029/2023GC011234
