What is the intuitive meaning behind plugging a random variable into its own pdf or cdf? A pdf is usually written as $f(x|\theta)$, where the lowercase $x$ is treated as a realization or outcome of the random variable $X$ which has that pdf. Similarly, a cdf is written as $F_X(x)$, which has the meaning $P(X<x)$. However, in some circumstances, such as the definition of the score function and this derivation that the cdf is uniformly distributed, it appears that the random variable $X$ is being plugged into its own pdf/cdf; by doing so, we get a new random variable $Y=f(X|\theta)$ or $Z=F_X(X)$. I don't think we can call this a pdf or cdf anymore since it is now a random variable itself, and in the latter case, the "interpretation" $F_X(X)=P(X<X)$ seems like nonsense to me.
Additionally, in the latter case above, I am not sure I understand the statement "the cdf of a random variable follows a uniform distribution". The cdf is a function, not a random variable, and therefore doesn't have a distribution. Rather, what has a uniform distribution is the random variable transformed using the function that represents its own cdf, but I don't see why this transformation is meaningful. The same goes for the score function, where we are plugging a random variable into the function that represents its own log-likelihood.
I have been wracking my brain for weeks trying to come up an intuitive meaning behind these transformations, but I am stuck. Any insight would be greatly appreciated!
 A: Like you say, any (measurable) function of a random variable is itself a random variable. It is easier to just think of $f(x)$ and $F(x)$ as "any old function". They just happen to have some nice properties. For instance, if $X$ is a standard exponential RV, then there's nothing particularly strange about the random variable
$$Y = 1 - e^{-X}$$
It just so happens that $Y=F_X(X)$. The fact that $Y$ has an Uniform distribution (given that $X$ is a continuous RV) can be seen for the general case by deriving the CDF of $Y$. 
\begin{align*}
F_Y(y) &= P(Y \leq y) \\
&= P(F_X(X) \leq y) \\
&= P(X \leq F^{-1}_X(y)) \\
&= F_X(F^{-1}_X(y)) \\
&= y
\end{align*}
Which is clearly the CDF of a $U(0,1)$ random variable. Note: This version of the proof assumes that $F_X(x)$ is strictly increasing and continuous, but it's not too much harder to show a more general version.
A: A transform of a random variable $X$ by a measurable function $T:\mathcal{X}\longrightarrow\mathcal{Y}$ is another random variable $Y=T(X)$ which distribution is given by the inverse probability transform
$$\mathbb{P}(Y\in A) = \mathbb{P}(X\in\{x;\,T(x)\in A\})\stackrel{\text{def}}{=} \mathbb{P}(X\in T^{-1}(A))$$
for all sets $A$ such that $\{x;\,T(x)\in A\}$ is measurable under the distribution of $X$.
This property applies to the special case when $F_X:\mathcal{X}\longrightarrow[0,1]$ is the cdf of the random variable $X$: $Y=F_X(X)$ is a new random variable taking its realisations in $[0,1]$. As it happens, $Y$ is distributed as a Uniform $\mathcal{U}([0,1])$ when $F_X$ is continuous. (If $F_X$ is discontinuous, the range of $Y=F_X(X)$ is no longer $[0,1]$. What is always the case is that when $U$ is a Uniform $\mathcal{U}([0,1])$, then $F_X^{-}(U)$ has the same distribution as $X$, where $F_X^{-}$ denotes the generalised inverse of $F_X$. Which is a formal way to (a) understand random variables as measurable transforms of a fundamental $\omega\in\Omega$ since $X(\omega)=F_X^{-}(\omega)$ is a random variable with cdf $F_X$ and (b) generate random variables from a given distribution with cdf $F_X$.)
To understand the paradox of $\mathbb{P}(X\le X)$, take the representation
$$F_X(x)=\mathbb{P}(X\le x)=\int_0^x \text{d}F_X(x) = \int_0^x f_X(x)\,\text{d}\lambda(x)$$if $\text{d}\lambda$ is the dominating measure and $f_X$ the corresponding density. Then
$$F_X(X)=\int_0^X \text{d}F_X(x) = \int_0^X f_X(x)\,\text{d}\lambda(x)$$
is a random variable since the upper bound of the integral is random. (This is the only random part of the expression.) The apparent contradiction in $\mathbb{P}(X\le X)$ is due to a confusion in notations. To be properly defined, one needs two independent versions of the random variable $X$, $X_1$ and $X_2$, in which case the random variable $F_X(X_1)$ is defined by$$F_X(X_1)=\mathbb{P}^{X_2}(X_2\le X_1)$$the probability being computed for the distribution of $X_2$.
The same remark applies to the transform by the density (pdf), $f_X(X)$, which is a new random variable, except that it has no fixed distribution when $f_X$ varies. It is nonetheless useful for statistical purposes when considering for instance a likelihood ratio $f_X(X|\hat{\theta}(X))/f_X(X|\theta_0)$ which 2 x logarithm is approximately a $\chi^2$ random variable under some conditions. 
And the same holds for the score function$$\dfrac{\partial \log f_X(X|\theta)}{\partial  \theta}$$which is a random variable such that its expectation is zero when taken at the true value of the parameter $\theta$, i.e.,$$\mathbb{E}_{\theta_0}\left[ \dfrac{\partial \log f_X(X|\theta_0)}{\partial  \theta}\right]=\int \dfrac{\partial \log f_X(x|\theta_0)}{\partial  \theta}f_X(x|\theta_0)\,\text{d}\lambda(x)=0$$
[Answer typed while @whuber and @knrumsey were typing their respective answers!]
