Map voter and candidate positions in a two-dimensional policy space. Calculate Euclidean distances and predict vote choices based on proximity to candidates.
| Constant | Symbol | Value |
|---|---|---|
| Speed of light | c | 2.99792458×10⁸ |
| Planck's constant | h | 6.62607015×10⁻³‴ |
| Boltzmann constant | kʙ | 1.380649×10⁻²³ |
| Avogadro's number | Nₐ | 6.02214076×10²³ |
| Gravitational constant | G | 6.6743×10⁻¹¹ |
| Gas constant | R | 8.31446 |
| Elementary charge | e | 1.602176634×10⁻¹⁹ |
| Electron mass | mₑ | 9.1093837015×10⁻³¹ |
| Proton mass | mₚ | 1.67262192369×10⁻²⁷ |
| Fine-structure constant | α | 7.2973525693×10⁻³ |
Map voter and candidate positions in a two-dimensional policy space. Calculate Euclidean distances and predict vote choices based on proximity to candidates
Each component has a specific meaning:
Note: Interpret the policy spatial model result against the clinical thresholds and context described above.
Enter the proximity for the patient or scenario you are assessing. Map voter and candidate positions in a two-dimensional policy space. Calculate Euclidean distances and predict vote choices based on proximity to candidates. Use the policy spatial model result to inform your clinical assessment.