The challenge will be to fix this whilst keeping the precise, mechanical and quantitative spirit of science alive.
The reason why scientists have a natural "disdain" for sociological studies, is because they don't have these qualities. Many of the arguments aren't convincing from a critical angle, and only convincing to people who have a pre-existing bias towards certain conclusions. Yet the scientific culture of today is falling into that trap itself.
The article criticises metric-driven incentives, but it is not metrics (the general concept) that are at fault. It is the choice of metric, and the meta-analysis of this that is lacking. These choices are themselves often backed up by non-scientific vague arguments, of the similar sort that scientists often criticise other fields as depending upon.
We must certainly not conclude from these studies that quantitative analysis is itself what is at fault. I know the article doesn't explicitly say this, but it hints at it - using suggestive phrasing like "disdained sociological studies" and referring to all "metric incentives" as a single group - and it is a point I have seen made by many non-scientists. That is, using these flaws as a straw man to attack the very qualities of what has made science so successful and useful.
To improve the situation, we must reject these straw-man arguments against science, and develop better methods that are more quantitive, over a broader spectrum of what is being analysed, and that are more self-critical.
The reason why scientists have a natural "disdain" for sociological studies, is because they don't have these qualities. Many of the arguments aren't convincing from a critical angle, and only convincing to people who have a pre-existing bias towards certain conclusions. Yet the scientific culture of today is falling into that trap itself.
The article criticises metric-driven incentives, but it is not metrics (the general concept) that are at fault. It is the choice of metric, and the meta-analysis of this that is lacking. These choices are themselves often backed up by non-scientific vague arguments, of the similar sort that scientists often criticise other fields as depending upon.
We must certainly not conclude from these studies that quantitative analysis is itself what is at fault. I know the article doesn't explicitly say this, but it hints at it - using suggestive phrasing like "disdained sociological studies" and referring to all "metric incentives" as a single group - and it is a point I have seen made by many non-scientists. That is, using these flaws as a straw man to attack the very qualities of what has made science so successful and useful.
To improve the situation, we must reject these straw-man arguments against science, and develop better methods that are more quantitive, over a broader spectrum of what is being analysed, and that are more self-critical.