It’s often assumed that a problem must be understood and explained before it can be solved. Let’s call this view the linear model problem solving, a la Roger Pielke Jr.’s linear model of science policy. This model is definitely true for some issues. Some problems are hidden and will not be addressed until they are brought to light.
But all social problems aren’t like this. Sometimes the problem is already well known. A few details may be missing and not fully fleshed out. But people who care more or less know it exists. For these types of problems, the solution cannot be separated from its understanding and description. You’ll go nowhere unless you give implementing a solution as much thought as describing and understanding.
Consider climate change. David Victor, Richard Bendick, Roger Pielke Jr. and others have argued that climate policy has suffered because scientists have focused too much on the latter and not enough on implementation. The realities of international law and the concerns of local decision-makers should have been accounted for early and often. Academics needed to focus on what what should people do about climate change.
I’m starting to think diversity in science suffers from the same problem. It’s possible I’m missing it, but most of what I read is problem definition and the value of diversity. I agree with all that. But if I’m trying to hire a new PHP developer or database engineer, and I have a stack of 50 resumes in front of me, what exactly should I do? How, specifically, should I go about diversity?
When we think about the details of implementation, it’s especially important to consider potential allies. It’s why I think we who care about racial and gender diversity in science could build a stronger coalition if we stressed personality and thinking styles more.