Is there any conventional small, medium and large effect sizes for ordinal logistic regression?

I was performing a power analysis of articles published in a journal of management using the pwr package in R. However, it seemed to be impossible to compute power for small, medium and large effect sizes for multiple ordinal logistic regression. I have tried using G*power, but it only seemed to be useful when we have simple logistic regression output. Thus, I have tried to simulate to calculate the power based on the answers by @GregSnow and @gung here: Simulation of logistic regression power analysis - designed experiments. How can I get power for small, medium and large effect sizes in multiple ordinal logistic regression?

Simplified notions of effect size just get us into trouble, and there are no cutoffs that work universally. I usually specify an odds ratio and distribution for a predictor for ordinal regression. For the proportional odds case, power of a simple unadjusted 2-sample comparison can be computed using the R Hmisc package popower function.