That 3M mask looks to be the same make and model as the one that I use for woodworking, which came from a local hardware store. Please support your local hardware store over Jeff Bezos if you can! (You might still need to order the right kind of filters online.)
This would have persuaded me to get a mask if I didn't already own one, and has persuaded me that I should get appropriate filters.
It would be easy to be nerd-sniped into listing all of the advantages and disadvantages of owning a mask and filters, but a major disadvantage that you didn't consider is the space that it takes up, not just physically but mentally (to remember where you put it). Again, it'd be easy to get nerd-sniped into a discussion of just how much time/effort the longterm storage of an item of this size takes, but I expect that this varies greatly from person to person. I suspect that this is the main impediment, for most people, against having items whose expected value lies in a small chance to be very useful.
I think that representation is best explained as both correspondence and the outcome of optimization - specifically, representation is some sort of correspondence (which can be loose) that is caused by some sort of optimization process.
I'll speak primarily in defense of correspondence since I think that is where we disagree.
"All models are wrong, but some are useful" is a common aphorism in statistics, and I think it is helpful here too. You seem to treat mistaken representations as a separate sort of representation. However, even an ordinarily correct representation can contain some mistaken elements. For example:
Likewise ordinarily mistaken representations can contain correct elements:
There are also edge cases where a representation mixes correct and incorrect elements, such that it isn't clear whether we should call it a mistaken representation or not:
This suggests that it is useful to stop thinking about mistaken and correct representations as separate types, but rather to think about representations having mistaken and correct elements.
Having made this shift, I think that the correspondence theory of representation becomes viable again. Even a representation that is conventionally classified as mistaken may contain many correct elements - enough correct elements to make it about whatever it is about. A child's representation of Santa Claus contains many correct elements (often wears red, jolly, brings presents) and one very prominent incorrect element (the child thinks that Santa physically exists rather than being a well-known fiction). It may very well be the case that most of the bits in the child's representation are correct; we just pay more attention to the few that are wrong. For another example: if I think that I see a horse, but I actually see a cow at night, the correct elements include "it looks like a horse to me," and "it's that thing over there that I'm looking at right now." There's a lot of specificity in that last correct element! I think that's enough to make my representation be about the cow.
On the other hand, we can consider examples where an optimization process exists but where it fails to create correspondence:
With that being said, I do agree with you that optimization is an important piece of the puzzle - but not becasue it can explain how something can be about something else even if it is mistaken. Rather, I think that optimization is the answer to the problem of coincidences. For example:
Adding the second criteria - that the correspondence must be caused by an optimization process - prevents a definition of representation from identifying coincidences as representations.
Upvoted for the addition to our collective and to my personal vocabulary. I've encountered a small number of people who fit this pattern (different context; almost certainly nobody you know) and it's helpful to have a memorable cognitive handle for it.
This post feels like it may have been written in response to some specific interpersonal drama. If it was, then I'd like to make it clear that I have absolutely no idea what it was and therefore no opinion on it. I just think this is a useful concept in general.
I do have one minor nitpick:
...and three people complain of deeply unpleasant experiences with one of the organizers.
It's not clear to me whether, in this example, all three complaints are about the same organizer. It seems like they probably are, from context, but this could be written more clearly.
That's a short question with a long answer. For brevity, we can just say that I'd prefer that money go to businesses which support a traditional retail model, which often include locally-owned small businesses, which often face competition within their niche, rather than supporting a company which isn't that - because this is (slightly) better for humankind in the long run.
If you disagree . . . well, the request still stands as a request.
It seems that you're right - if you'll forgive the n=1 sample size, I can get P100 filters at the same store as the mask. This surprised me, as I'd expect that filters that are fine enough to be effective against viruses would be a separate and more specialized item than the filters which are used for industrial hazardous particle filtration.
Since I was surprised, I've done a little more digging. I'd like to hear more about why you expect P100 filters to be effective in a potential pandemic, and how effective you think that they will be.
P100 filters are tested against particles 0.3 microns in diameter. This is the same standard as N95 masks. Compare that to the size of viruses in a table of representative key viruses in a textbook, or the recent coronavirus. Yes, some viruses are big enough to be mechanically filtered; most aren't. Yet N95 masks are effective against smaller particles down to the size of a typical virus; this is achieved through methods other than mechanical filtration, such as electrostatic attraction. (Notably, under some conditions they become more effective as particle size decreases.) This study has a roughly similar setup for P100 rather than N95 respirators. It isn't quite an apples-to-apples comparison because the particle type is different. However, it's noteworthy that (arbitrarily choosing a 100 nm particle, 30 L/min median inspiratory flow, taking the average of the different breaths/min tested) P100 filters do worse by a factor of about 10 than N95! This doesn't change much if you look at particles down to about 50 nm and up to about 200 nm (which is as high as the second study goes).
Perhaps whatever is done to filters to make them oilproof (which is what the "P" stands for) happens to impair their effectiveness against smaller particles? Maybe it interferes with the aforementioned electrostatic attraction?
Going from n=1 again, the store that stocks P100 filters doesn't carry N95 filter for that mask. Do you recommend P100 filters rather than N95 due to availability? Since we're using these filters outside of their rated specifications (we're interested in smaller particles), this might be a case where bigger numbers aren't actually better.