Binary classification: is there a tradeoff between predicting class 1 vs 0?
To me, an algorithm is considered having an 'edge' for a given class if the accuracy on that class is higher than the proportion of that class in the dataset. E.g : if we have 20% Class A and 80% Class B, I would consider the model being 'performant' if accuracy on class A is >20%, or if accuracy on Class B is >80%.


Consider the following. Your 80% class is 0. You predict a constant 0 for all datapoints. You have 80% accuracy already with no modelling done and 100% accuracy on the majority class. If you fit a model to that data, you'll get an accuracy above 80% on the majority class and lesser accuracy on the minority class, and that's not a good performance which debunks your theory of a good metric.

The case above is from personal experience, seeing your results makes me think if your datapoints are really separable.
It is possible to have accuracies above the distribution marks if your features are good enough for model to use to separate each class from the other which in reality is feasable.

And again the choice of the metric in binary classification will always be a business problem, what is the cost of misclassifying a 1 as a 0 and a 0 as a 1? If the answer to these two questions is not the same, you should have some specific metrics for it.


  Hopefully the meta-ensemble will have a value above 0 for all classes for my made up metric.


It really depends on how diverse your base models are. But in general, meta-learners made out of weak learners outperform a single complex model. So a sort of a yes to your question.