I agree that if we had a healthy field, then the field should move to correct wrong information.
That being said, I don’t expect professors to understand the evidence for the core beliefs in their field, chiefly because I do not see where in the doctoral path such a comprehensive historical survey would take place. I expect them to know what the conclusions are, but no particular details about how they were reached, and to know even those chiefly through references. Even physics is shot through with apocrypha and folk tales about fundamental findings. I only expect a professor to know about the evidence for the core of their specialization, because this much is included in graduate course curricula I have seen.
At least as far as the United States is concerned, my default expectation regardless of field is that they will fail to immediately catch an error like happened with covid unless it directly conflicts with standard references in the field.
To sum up: I agree, but I think this problem affects basically all academic fields, rather than virology being especially deficient.
If you wanted to know the truth, you would need to start with all the raw data and recalculate your conclusions.
But since the raw data is often old, and only “positive” data was published and “negative” data not, and collected usually by poorly paid humans...you need to throw it all out and start over.
Would help to have enormous amounts of robotics to make this possible in a short timespan.
I assume that this is what you would need to do to solve “difficult” problems, such as biology.
I think about this periodically in the context of likelihood functions. My bet for the biggest problem is that there isn’t prior work for people to cite or data for them to build on, so it seems to me a good thing to do would be to things like:
Find good public datasets to run likelihood functions against.
Run very basic experiments in huge numbers to get strong effect sizes for fundamental findings.
I think the robotic laboratories would be a great fit for 2.
I agree that if we had a healthy field, then the field should move to correct wrong information.
That being said, I don’t expect professors to understand the evidence for the core beliefs in their field, chiefly because I do not see where in the doctoral path such a comprehensive historical survey would take place. I expect them to know what the conclusions are, but no particular details about how they were reached, and to know even those chiefly through references. Even physics is shot through with apocrypha and folk tales about fundamental findings. I only expect a professor to know about the evidence for the core of their specialization, because this much is included in graduate course curricula I have seen.
At least as far as the United States is concerned, my default expectation regardless of field is that they will fail to immediately catch an error like happened with covid unless it directly conflicts with standard references in the field.
To sum up: I agree, but I think this problem affects basically all academic fields, rather than virology being especially deficient.
If you wanted to know the truth, you would need to start with all the raw data and recalculate your conclusions.
But since the raw data is often old, and only “positive” data was published and “negative” data not, and collected usually by poorly paid humans...you need to throw it all out and start over.
Would help to have enormous amounts of robotics to make this possible in a short timespan.
I assume that this is what you would need to do to solve “difficult” problems, such as biology.
I think about this periodically in the context of likelihood functions. My bet for the biggest problem is that there isn’t prior work for people to cite or data for them to build on, so it seems to me a good thing to do would be to things like:
Find good public datasets to run likelihood functions against.
Run very basic experiments in huge numbers to get strong effect sizes for fundamental findings.
I think the robotic laboratories would be a great fit for 2.