CSP optimization problem is approximation resistant if it is $NP$-hard to beat the approximation factor of a random assignment. For instance, MAX 3-LIN is approximation resistant since a random assignment satisfies $1/2$ fraction of the linear equations but achieving approximation factor $1/2+ \epsilon$ is $NP$-hard.
MAX CUT is a fundamental $NP$-complete. It can be formulated as CSP problem of solving linear equations modulo 2 ($x_i + x_j= 1$ mod 2). A random assignment achieves $1/2$-approximation factor (of the total number of edges $|E|$). Haglin and Venkatesan proved that achieving an approximation factor $1/2+ \epsilon$ is $NP$-hard (i.e. finding a cut better than $|E|/2$). However, Hastad showed that MAX CUT is not approximable to $16/17+ \epsilon$ factor within the optimal cut unless $P=NP$. Goemans and Williamson gave a SDP-based polynomial time algorithm with 0.878 approximation factor (within the optimal cut) which is optimal assuming the Unique Games Conjecture. It seems to me that expressing the approximation factor relative to total number of constraints ($|E|$) is more natural and consistent with the convention used for MAX 3-LIN problem.
Why is the approximation factor for MAX CUT given relative to the size of optimal cut instead of the number of constraints (# of edges)? Am I right in concluding that MAX CUT is approximation resistant when the approximation factor is relative to the total number of constraints ($|E|$)?