Gettiers in software engineering (2019)

Hmm, are there better cases that disprove JTB? Couldn't one argue that the reliance on a view that can't tell papermache from a cow is simply not a justified belief?

Is the crux of the argument that justification is an arbitrary line and ultimately insufficient?

I was thinking that one solution might be to specify that the "justification" also has to be a justified true belief. In this case, the justification that you see a cow isn't true, so it isn't a JTB.

Of course that devolves rapidly into trying to find the "base case" of knowledge that are inherent

Yes, the paper itself is much more unambiguous (and very short): https://courses.physics.illinois.edu/phys419/sp2019/Gettier....

These are correct but contrived and unrealistic, so later examples are more plausible (e.g. being misled by a mislabelled television program from a station with a strong track record of accuracy).

The point is not disproving justified true belief so much as showing the inadequacy of any one formal definition: at some point we have to elevate evidence to assumption and there's not a one-size-fits-all way to do that correctly. And, similarly to the software engineering problems, a common theme is the ways you can get bitten by looking at simple and seemingly true "slices" of a problem which don't see a complex whole.

It is worth noting that Gettier himself was cynical and dismissive of this paper, claiming he only wrote it to get tenure, and he never wrote anything else on the topic. I suspect he didn't find this stuff very interesting, though it was fashionable.

I like the example of seeing a clock as you walk past. It says it's 2:30. You believe that the time is 2:30. That seems like a perfectly reasonable level of justification -- you looked at a clock and read the time. If unbeknownst to you, that clock is broken and stuck at 2:30, but you also just happened to walk by and read it at 2:30, then do you "know" that it's 2:30?

I think a case can't so much "disprove" JTB, so much as illustrate that adopting a definition of knowledge is more complex than you might naively believe.

This and many other suggestions have been explored, and usually found wanting (see e.g. https://plato.stanford.edu/entries/knowledge-analysis/#NoFal...).