Logistic regression, log-odds score interpretation If I model glm2.out <- with(D1train, glm(non_def ~ loan_amnt + grade + emp_length_p + term + state_woe, family=binomial("logit"))) Where non_def is a column of values with $1$ if I do not default on a loan, and $0$ if I do default.
The question is, does a higher score indicate a greater association with defaulting or non-defaulting?

Your coding of your outcome variable is unorthodox. Most would code defaulting with $1$ rather than with $0$. Since you have non-defaulting as $1$, the mode is predicting the log-odds of non-default.
$$
\log\left(
\dfrac{
\hat p_{non-default}
}{
1-\hat p_{non-default}
}
\right)=X\hat \beta
$$
The log-odds function and its inverse are strictly increasing functions of the probability of non-default. Consequently, increasing the $X\hat\beta$ linear predictor increases the estimated probability of non-default.
