I haven't done an existing project yet, but based on some experiments it seems like I'd mostly be deleting configuration files and making an import map. A simple SPA is crazy easy in deno: https://github.com/baetheus/deno-spa-example/
Deno can handle plain JavaScript just fine. In what way is targeting TypeScript harmful? How do you differentiate between a project that can benefit from type safety and one that cannot? Why should Deno's documentation be in JavaScript?
What does is mean to "respect the devs right to choose which ever language they want"?
If you are concerned about security in one of your imported dependencies (or in user code) you can run that code in a sandboxed worker (See: https://deno.land/manual@v1.18.0/runtime/workers#specifying-...). Beyond this it is still very useful to limit your runtime for software to only access specific directories, network addresses, etc. So the sandbox/security is definitely not useless.
Considering that Ryan Dahl started both Node.js (where imports do not include file extensions) and Deno (where he added them back after deciding it was a bad decision to leave them off) I'm not sure how you've come to the conclusion that TypeScript/Node does importing correctly. Additionally, the ecmascript import syntax https://developer.mozilla.org/en-US/docs/Web/JavaScript/Refe... specifically mentions that your tooling may or may not need file extensions but the examples in the spec (and indeed in browsers at large) require a full or relative path including the extension. If anything, I'd bet that file extensions make it into Node and tsc in the next few years.
1. The author (David Zweig) points to https://www.theatlantic.com/ideas/archive/2021/09/school-mas... as proof of skepticism about the costs and benefits of mandating that children 2-12 wear masks in school. That article begins with the statement "The potential educational harms of mandatory-masking policies are much more firmly established, at least at this point, than their possible benefits in stopping the spread of COVID-19 in schools." but ends with the statement "Do the benefits of masking kids in school outweigh the downsides? The honest answer in 2021 remains that we don’t know for sure." The author has failed to present a coherent thesis for their skepticism, and as it reads it appears that their view actually softens by the end of the piece.
2. David Zweig begins their second critique of the Arizona study with, "This estimated effect of mask requirements—far bigger than others in the research literature—would become a crucial talking point in the weeks to come." yet this is a fundamental misread of the study that happens often in science reporting. They study actually says, "In the crude analysis, the odds of a school-associated COVID-19 outbreak in schools with no mask requirement were 3.7 times higher than those in schools with an early mask requirement (odds ratio [OR] = 3.7; 95% CI = 2.2–6.5). After adjusting for potential described confounders, the odds of a school-associated COVID-19 outbreak in schools without a mask requirement were 3.5 times higher than those in schools with an early mask requirement (OR = 3.5; 95% CI = 1.8–6.9)." This is not a causal statement, it is a correlative one. This is to say that the study does not make the point that the mask mandates cause reduction in covid outbreaks, but that those schools with mask mandates clearly experience fewer outbreaks. This conflation is made many times throughout the rest of the article. It matters because, like most science, the study adds to our understanding of reality, and if we misunderstand the science it's likely we are misunderstanding reality.
3. Jonathan Ketcham is quoted, at first without an actual point, as saying: "You can’t learn anything about the effects of school mask mandates from this study". This isn't really an argument so it's interesting that it's made it into the an article on the science section of the Atlantic. It also shows that Jonathan Ketcham may have conflated causation and correlation as well. Without naming them or providing any other attribution, the article then makes an implicit argument from Ketcham's quote: "His view echoed the assessment of eight other experts who reviewed the research, and with whom I spoke for this article. Masks may well help prevent the spread of COVID, some of these experts told me, and there may well be contexts in which they should be required in schools. But the data being touted by the CDC—which showed a dramatic more-than-tripling of risk for unmasked students—ought to be excluded from this debate." So the experts interviewed all agreed that masks "may well help prevent the spread of covid" and that there "may well be contexts in which they should be required in schools", but the data in the study we are citing should be excluded from the public discourse. To me, this section requires a lot of brain twisting work. The experts (even those critiquing this study) say masks help (this is not a may well statement). The experts say we might want to mandate masks in schools. But for some reason, this study that finds some valid correlations shouldn't be talked about in the public debate. I find this nonsensical considering the article that wants the public to not use this research in debate must use the research to achieve its goal. If the author really wanted that data to go away it should be doing what any good scientist would do, which is more science. Get some more data, show that the models presented by the study are in fact wrong.
4. Noah Haber is quoted as saying that the research is, "so unreliable that it probably should not have been entered into the public discourse." Great quote for a science article. It's got a lot of information to sink your teeth into. At this point in the article I find that I'm very annoyed by the lack of actual science reporting. It mostly reads as shit stirring to me.
5. David Zweig makes the argument that some schools are open for six weeks instead of the three. The article has some ambiguity here due to the writing style. Zweig states, "After reviewing school calendars and speaking with several school administrators in Maricopa and Pima Counties, I found that only a small proportion of the schools in the study were open at any point during July. Some didn’t begin class until August 10; others were open from July 19 or July 21. That means students in the latter group of schools had twice as much time—six weeks instead of three weeks—in which to develop a COVID outbreak." It is unclear whether the "latter group of schools" here refers to those open on July 19/20 or to the group of schools with mask mandates. I think it's referencing the dates. Megan Jehn, one of the study's authors responded to this by saying that the median start date for the non-mandate schools was August 3rd and those with mandates started on average on August 5th. She also responded with, "It is highly improbable that this difference alone could explain the strong association observed between mask policies and school outbreaks." I would like to see more disaggregated data in the study but at some point you've gotta believe an epidemiologist when they tell you that your counter point isn't so good. However, Ketcham is quoted again here saying, "If schools with mask mandates had fewer school days during the study, that alone could explain the difference in outbreaks." I agree with Ketcham, that could explain the difference in outbreaks. Does it, though? The authors of the study, who have done the work in this case say its improbable, the economist Jonathan Ketcham says it might explain the diffence. I'm not sure this qualifies as a severe issue in the study, but it's something that could likely be put to rest with a little more public data.
6. Louise-Anne McNutt and Ketcham point to a possible detection error in the study. Namely, "...according to Maricopa County guidelines, students are considered 'close contacts' of an infected student—and thus subject to potential testing and quarantine—only if they (or that infected student) were unmasked. As a result, students in Maricopa schools with mask mandates may have been less likely than students in schools without mandates to get tested following an initial exposure." To this the study authors responded that it is, "highly speculative to make the assumption that identified close contacts are more likely to be tested than other students." This is a little harder to muddle through as we are getting a core argument on designing a reliable study. So, we are looking at the definition of an outbreak. The study states its definition thusly, "A school-associated outbreak was defined as the occurrence of two or more laboratory-confirmed COVID-19 cases§ among students or staff members at the school within a 14-day period and at least 7 calendar days after school started, and that was otherwise consistent with the Council for State and Territorial Epidemiologists 2020 outbreak definition¶ and Arizona’s school-associated outbreak definition." The CSTE defines an outbreak thusly, "Two or more laboratory-confirmed COVID-19 cases among students or staff with onsets within a 14-day period, who are epidemiologically linked, do not share a household, and were not identified as close contacts of each other in another setting during standard case investigation or contact tracing." The confounding factor that McNutt and Ketcham are referring to, which is that if two people are considered an outbreak they could have caught covid from outside of the school instead of each other, is sort of moot here as the paper states they conducted adjusted logical regression analysis against many factors including the, "7-day COVID-19 case rate in the school’s zip code during the week school commenced". That means that detection bias should not be present and we can be fairly certain that the definition of outbreak in this study is both consistent and likely means that the two or more cases are in fact school related.
7. Jason Abaluck makes the point that vaccination status could be a confounding variable. It can be! This is a flaw in the study that the study itself acknowledges. I'm not sure how this is an argument for the study being junk science. It's a little like one of the first probability and statistics examples I was taught. In this example a study was done on Coney Island where the scientists looked at ice cream sales and the number of drownings in the area. They found that there is a clear correlation between them as when ice cream sales went up so did drownings. In the study they unfortunately could not get access to temperature data or visitor counts. They concluded that ice cream sales are a good indicator of drownings. Of course, the reason ice cream sales and drownings are correlated is because people go to Coney Island when it is hot out. They also eat ice cream when it is hot out. With more people going to Coney Island there are more people that are likely to drown. The study did not make the claim that ice cream sales cause drownings, just like this study does not claim that mask mandates reduce covid outbreaks. However, in both cases the indicator is useful. In the case of mask mandates we also have data that shows masks reduce covid transmission in many other scenarios. So, yes, vaccination status is a known confounding variable that I'm sure the authors of the study would like to control for. Since they can't they did the next best thing and established that mask mandates are a good indicator for reduced covid outbreaks.
8. David Zweig attempts to reproduce some of the data himself. He attempts to build part of the data set used in the study for Maricopa county. In the end he gets the list of schools from the study authors. He writes, "Yet it still included at least three schools in Pima County, along with at least one virtual academy, one preschool, and more than 80 entries for vocational programs that are not actual schools. In response to a follow-up inquiry, they acknowledged having included the online school by mistake, while attributing any other potential misclassifications to the Arizona Department of Education." This is interesting, but ultimately the misclassification of some schools does not ultimately change the statistical methods used.
This is it. Eight arguments meant to show, "...the study’s methodology and data set appear to have significant flaws." I don't see the points made as exposing significant flaws. However, I am a simple programmer who studied mathematics and reads technical specifications in my spare time.
At the end of the day please remember these truths. Covid kills a lot of people, mainly the older ones. Covid transmits via aerosolized bodily fluids (coughs, sneezes, breathing), and things like wiping your nose then touching doorknobs where someone does the same in reverse order. Masks, preferably well fitting n95 and kn95 ones, irrefutably reduce the spread of covid. Washing your hands well and often irrefutably reduces the spread of covid. The science here is icing on the cake and it just tells us, sometimes roughly, how much these things help. Beyond those two things the vaccines are an entirely different story (because thanks Trump), but the science is more clear there, they also help.
He covers a few additional parts of the typestate pattern, such as isolating data in specific states as well as sharing common implementations across a subset of states.
I'd also like to note that typestates also show up in functional programming under Indexed Monads, where a function might take a struct from an initial typestate, unwrap its data, and return a final(likely different) typestate. You can search Indexed Monad for more explanation there. If you work primarily in typescript you can find a production ready implementation of typestate programming here: https://github.com/DenisFrezzato/hyper-ts
My point is that all methods and techniques probably have worked for someone doing something.
And I should have leaned in on how much is still left to implementation in terms of "shed." From weather, to what is being stored. It isn't like there is a universal shed design that will make everyone happy.
Nor is this saying that some things aren't truly valuable. Just recognize that some places they don't help as much as you would like. This isn't saying they are bad or worthless. Just acknowledging that they are oversold.
It seems that maybe you didn't read the entire essay as the actual argument it makes is that the dominating theory (which you fabricated and then quoted as if it were from the essay) is incorrect. In my reading, the error of obesity research is that it focuses on energy-balance instead of studying the why and how of fat accumulation in humans. To actually quote the essay:
"For researchers and public health authorities to make real progress against obesity and the obesity‑diabetes epidemics, they will have to shed their energy-balance thinking, their obsessive focus on how much people eat and exercise. Instead, borrowing again from Hilde Bruch in 1957, they’ll have to focus on fat metabolism and storage itself, “since by definition excessive accumulation of fat is the underlying abnormality.” If they think of obesity as it simply and clearly is, a disorder of excess fat accumulation, they might actually figure it out."
First - yes, I did read the entire essay reasonably carefully. I've been reading this same essay from Taubes/Ludwig for at least 5 if not 8 years. [Edit - Make that 10 years - according to Amazon, I bought "Why we get Fat" in Feb, 2011 - and remember it being revolutionary] The thing is I agree with them wholeheartedl, and have from the first time I read stuff from Taubes 10 or so years ago.
Saying that weight increase is a result of excess caloric consumption is in some ways a distraction, because of course that's the reason why weight increase occurs. The real issue is what does drive caloric consumption, and the side issue is what impact do dietary components have on insulin reactions, metabolic reaction, satiation, etc....
What kind of drives me nuts is how they have an issue with that fundamental, never disproven in a single controlled clinical-condition study, statement of "An excess of caloric input over caloric output will result in weight gain. " It's fine to start off with that, and then move onto the more important questions.
I mean, they could start off by saying, "This statement may be true, but it's only relevant in a laboratory, and mostly irrelevant in free-living scenarios." I would agree with that as well. I think it was Taubes that once wrote, "Our (predetermined) weight determines how much we eat, not the inverse" which I thought was quite clever and backed up with a lot of studies.
