I found this [2009 JSA talk][1] by Thad Tarpey to provide a useful explanation and commentary on the Box passage. He argues that if we regard models as approximations to the truth, we could just as easily call all models right.

Here’s the abstract:
> Students of statistics are often introduced to George Box’s famous
> quote: “all models are wrong, some are useful.” In this talk I argue
> that this quote, although useful, is wrong. A different and more
> positive perspective is to acknowledge that a model is simply a means
> of extracting information of interest from data. The truth is
> infinitely complex and a model is merely an approximation to the
> truth. If the approximation is poor or misleading, then the model is
> useless. In this talk I give examples of correct models that are not
> true models. I illustrate how the notion of a “wrong” model can lead
> to wrong conclusions.

  [1]: http://andrewgelman.com/wp-content/uploads/2012/03/tarpey.pdf