Peter wrote:
you can’t get some things peer reviewed if the only “peers” you can dig up (a) don’t like the topic (non PC, etc) or (b) don’t like you (academia has some highly competitive stuff going on, and you have CV19 researchers who hate some other CV19 researchers; there is a lot of prestige involved)
The peer-review systems is not perfect but your criticism is misdirected. For one thing “you” do not “dig up” the peers. That would obviously defeat the purpose. The reviewers are chosen by the editorial board of the journal (or other venue) where your paper will be published. Certainly, editors with an agenda could choose reviewers to steer the review in a particular direction, but that’s nothing the author has control over.
You can get anything “peer reviewed” if the “peers” receive their research grants from the right places It’s an old and well known problem in academic publishing.
I don’t understand what you’re saying here.
Cobalt wrote:
why did you summarise “While small benefits cannot be excluded, we do not find benefits on case growth (what is being called flattening the curve here) of more restrictive NPI’s (lockdowns). Similar reductions may be (not will) achieved by less restrictive measures”as
“there is no proof lockdown measures work”
Because there is no proof lockdown measures work? Malibuflyer wrote:
The problem will be – esp. as “Stanford” always sounds as great name – is that some media will turn that into almost the opposite of what the authors really said and title “Stanford researches have shown lockdowns are not effective”.
What the research says is that this Stanford research team does not believe lockdown measures are the right measures and that other measures may (again not will) be more effective. This is obviously taking into account a cost/benefit element which they lay out clearly in the research.
LFHNflightstudent wrote:
What the research says is that this Stanford research team does not believe lockdown measures are the right measures and that other measures may (again not will) be more effective. This is obviously taking into account a cost/benefit element which they lay out clearly in the research.
I don’t care what they believe and at least the research published in this paper doesn’t look at all at any cost implications.
What they did is to show that by comparing South Korea and Sweden (the only two countries they looked at without lockdown) to some European Countries with lockdown they did not find evidence that stay at home is effective. There are lot’s of potential explanations for this result of this specific analysis they did not control for.
Let’s stick with what they said: “THEY failed to find evidence”. That doesn’t say it is there and they didn’t even try to find evidence for the opposite. It’s just that with the methodology they used they did not find any.
Airborne_Again wrote:
The reviewers are chosen by the editorial board of the journal (or other venue) where your paper will be published. Certainly, editors with an agenda could choose reviewers to steer the review in a particular direction, but that’s nothing the author has control over.
The problem is that academia in each individual field is a very small community.
In theory it is exactly how you describe. In practice in many areas there are only a handfull of research groups globally that are at the very top level and therefore can really act as reviewer. Nature and Science don’t use the same reviewers. So by deciding if you hand in a paper at nature or at science you practically know already pretty well who will get it for review.
Plus: Those groups are all in competition to each other, because they are basically working on the same topics. And everyone knows that for major discoveries anyone of the main journals will publish to papers and not more. So if you really found something you would chose the journal where either the research group(s) where they get the reviewers from is either quite far away from similar discoveries (and therefore not in direct competition) or needs you as “second paper”. We spent a lot of time back then at MPI to strategize around that and I know all competing groups did as well….
The issue is inevitably the measure against which the success of NPI’s is judged.
For example, in a country where the health care capacity is very large, and the loss of life of elderly patients is not considered significant, then NPIs could be judged as not having a significant impact.
In a country where the health capacity is more limited, and there is a risk of its collapse with Covid patients, then NPIs would logically seem the only effective way of preventing this happening, but inevitably with lots of other devastating effects on the economy and the population’s mental health. You can judge the benefits dont outweigh the disadvantages, but such a judgement is undoubtedly subjective to your own priorities.
I have yet to see an argument in the UK why our health services would not have collapsed without the imposition of NPIs, and this seems even more true with the new variant where it seems clear the trajectory of cases without intervention would have caused the NHS to collapse. What alternatives measure are available? I am not aware of any.
Malibuflyer wrote:
The problem is that academia in each individual field is a very small community.
That depends on the field, of course, and what you mean by “very small”… I work in a specialised area of computer science (formal methods) but I can’t say that the community is small enough that I can control who will review my papers.
LFHNflightstudent wrote:
Because there is no proof lockdown measures work
The paper did not say that. This statement means that lockdowns do not work at all, when the paper said that lockdowns do not work better than other, less intrusive measures
Cobalt wrote:
The paper did not say that. This statement means that lockdowns do not work at all, when the paper said that lockdowns do not work better than other, less intrusive measures
No the paper says they cannot find any benefits from lockdown measures. How is that different from they do not work?
Malibuflyer wrote:
Let’s stick with what they said: “THEY failed to find evidence”. That doesn’t say it is there and they didn’t even try to find evidence for the opposite. It’s just that with the methodology they used they did not find any.
sure, but then it comes down to what you want to believe again. Hardly conclusive or an argument for the damage that is being done by implementing these measures for which there is no proof they actually work.
kwlf wrote:
There is even a point in your early 80s where your life expectancy rises slightly as you get older!
This is pure statistics. Lets say 50% of the population have a disposition for a particular disease, it could be environmental or inherited, but there is no way to detect it up front. This disease will lower the life expectancy to 70 years. The other 50% have no such disposition, and have a life expectancy of 100 years. Just to simplify, lets assume the life expectancy is 100%, you will die for sure, suddenly and unexpectedly at 70 by the disease, or equally suddenly at 100 of age.
Looking at the marginal life expectancy as you age, the people who are dispositioned will have a very short span when starting to reach 70. At 69, 50% have 1 year left, and the other 50% have 31 years left. The average is 16. At 71, all the diseased have died, and those left have a marginal life expectancy of 29 years. For the entire population the average rest of life expectancy has suddenly increased from 15 to 30 years when passing 70. Since you have no idea of which group you are part of this is 100% correct. At 69 you can expect to live another 16 years on average (actually 1 OR 31, 50/50), at 71 you will for sure live another 29 years.
On a side note, I think the same concept exists in GA fatality/accident rates. Some are more likely to mess up than others. Those who do, are the ones that create the statistics of deaths/accidents per hour flown, at least when pilot error of some kind is the cause. The rest will die of old age. Just because we don’t really know with any certainty which group we a part of, doesn’t mean we are not in the surviving group, and as such, the statistics has no relevance to us whatsoever. On the other hand, this doesn’t make us immune to freak accidents. I think it’s worth thinking about that statistics are only statistics, and it’s completely irrelevant to us until we are the ones creating it. There is no causality involved if we are in the surviving group, which most of us are.
