Well my main concern with this methodological leap is that it makes it data incomparable over years. IHMO, tracking progress is vital if we consider GODI as a way to open discussion about data publishing and evidence for advocacy.
Also, I am not sure if this new approach really tells us much about the actual availability of the data. I would prefer weighted assessment to have a clearer picture about the state of the data. For example indicate compliance with each criteria as marking it red/green and also show overall score. Than you can, by one glance get a much better idea on what is available. The current approach requires you to read a separate set of instructions to understand the result correctly. How many visitors of the site would actually bother with taking a look at it?
You mention that one of the reason for taking the “all or nothing” approach are specific use cases. But I could not find those examples (in the public GODI interface, i looked at just at a couple of underlying documents).
I also think that the usability info can suffer with this approach. In context of open data, I perceive usability as potential to reuse the data.
For example, Czech data about weather are scored 45% - criteria about precapitations etc are met, but the data are closed form both legal and technical point of view and scattered in multiple places of the site. So such data are probably quite helpful for someone who is just checking if it’s going to rain tomorrow. But pretty much useless for anyone who would wish to embody it in his/hers own analysis or mobile app.
Czech data about election results are available in machine readable format and downloadable in bulk under open licence. But also in XLS or HTML if non-tech people care to checkout detailed results for their village. Currently the dataset is missing invalid votes count (but there’s number of valid votes and sum of votes). But nonavailability of explicit number of invalid votes scores the whole dataset with 0%. A seemingly tiny detail can turn the value of the dataset upside down (last years data with same criteria got 100%).
So there is a big discrepancy between my grasp of usability of the data and the score. And this makes me believe than weighted assessment is much better.