Algebraic manipulation of normal, $\chi^2$ and Gamma probability distributions If $X_1, \ldots, X_n \sim N(\mu, \sigma^2)$, then 
$$
\frac{n - 1}{\sigma^2}S^2 \sim \chi^2_{n - 1}
$$ 
where $S^2 = \frac{1}{n-1}\sum_{i=1}^n (x_i^2- \bar{x})^2$, and there's a direct relationship between the $\chi^2_p$ and Gamma($\alpha, \beta$) distributions:
$$
\chi^2_{n - 1} = \text{Gamma}(\tfrac{n-1}{2}, 2).
$$
But then why is
$$
S^2 \sim \text{Gamma}(\tfrac{n-1}{2}, \tfrac{2\sigma^2}{n-1}) \,?
$$ 
And why do we multiply the reciprocal with $\beta$ and not $\alpha$?  Is it because $\beta$ is the scale parameter?  
In general, are there methods for algebraic manipulation around the "$\sim$" other than the standard transformation procedures?  Something that uses the properties of location/scale families perhaps?

It seems you are using the "shape" and "scale" parameters; then it's better to use the notion $\text{Gamma}(k, \theta)$. Here $k$ is the "shape parameter", and $\theta$ is the "scale parameter".
If $X\sim \text{Gamma}(k, \theta)$, then $cX\sim \text{Gamma}(k, c\theta)$ for any $c > 0$.
Multiplying by $c$ (stretching/shrinking) changes the scale, but not the shape.
In your case $X = \frac{(n - 1)}{\sigma^2}S^2$ and $c = \frac{\sigma^2}{(n - 1)}$.
