It has nothing to do with the fuel cost. This is what ryanair's website says about oversized luggage: "Passengers who bring an oversize bag (over 55x40x20cm) to the boarding gate will either have their bag refused or, where available, placed in the hold of the aircraft for a fee of £/€ 70.00 - £/€ 75.00."
Ryanair earns every time they identify an oversized bag at the boarding gate. They just share some of that money with their staff.
How does size of an item in the cabin correspond with excess fuel? In particular, how do large items cause excess fuel if they are in the cabin, but not the hold?
Remember that the mass of something is the same if it is in the hold or in the cabin, and just because something is large doesn't mean it necessarily has a large mass.
Only if the staff is fair. Frontier in America pays their staff $10 per bag identified and when they first started, some gate agents went wild trying to get that $10. There were videos of bags that cleanly fit in the sizer but passengers were being charged and threatened with being blocked from the flight if they didn't pay quickly.
I believe only certain versions and on certain architectures is seL4 verified for. There are no bugs found at the C source code level for these builds of seL4.
This issue appears to have affected all architectures. This issue was present in the specification against which seL4 was verified. You can say that there are no bugs by virtue of it following the specification, but if the specification was wrong and in this case it was, then were there really no bugs?
Even if it's not exactly seL4, there's good value in taking inspiration for design elements. It would still be a lot more robust than commodity operating systems.
This is silly, it assumes people will purchase instead. I pirate stuff because I am bored and just want to watch something, my order of priorities are:
Pirate, not watch, purchase.
Especially since the cheaper plane flights in Europe are absolutely painful, dropping you off 2 hours away from the city centre into some minor airport.
Taking the TGV from Paris to Zuerich in comparison is such a relief.
sigh this is just straight up wrong, I was an RA that worked on a real time social media analytics software. We were able to pick up on things like likely covid infections sites etc.
Academia has flooded the literature with >10,000 research papers based on the Twitter API feed. Virtually none of it is reproducible, it's frequently based on circular logic, the methodologies are unscientific and the conclusions are usually deeply partisan, but it nonetheless gets amplified by the media as "proof" of various false claims.
Count me in the camp of people who is happy Musk is doing this. I've been writing for years about the plague of "social bot" research coming out of academia that's based on the Twitter API:
Maybe your specific work on COVID was good, but it was certainly drowned out by the work that was sharply net negative for both society and science. Academic institutions were clearly never going to get the problem under control, so booting them out whilst allowing search engines and the like to continue accessing the feed seems like a good solution.
This is absurd; you're throwing the baby out with the bathwater. Certainly, it is easy to find social science papers with terrible methodology that use the Twitter API, or that build on the sand of papers with terrible methodology.
But you conclude from that that all academic use of the Twitter API is garbage, which is nonsensical, and that preventing academics from studying Twitter at scale is the ideal solution. Your hyperbolic language (here and in your two medium articles, which I read thoroughly, along with the SSRN paper you cited*) does nothing for your own credibility.
The main 'methodology' of the SSRN paper is combing through other papers' datasets, contacting some of the identified 'bot' accounts, and establishing that they're operated by real people; the accounts as misidentified as bots when in reality the account operators were just aggressively quote-tweeting by using copy & paste to spread (eg) political or Qanon messages 200 times an hour. The authors point out that by really making an effort, Twitter users can tweet spam up to 25 times a minute, with no bots in sight! While the authors are quite correct to point out that people can be misidentified as bots, this completely ignores the fact of the unwanted spamming behavior. Pointing out the scientific flaws of 'tools' like Botometer is wholly valid, but the effort to research and develop tools for bot identification are a response to the fact of systematic information pollution, and most papers that try to address this issue are careful to offer caveats and qualifications about the limitations of their methods. It is not the fault of academics if media pundits over-simplify the fruits of their research.
Here are some examples of high quality research using data from Twitter:
1. Ephemeral Astroturfing Attacks: The Case of Fake Twitter Trends
A good start! It makes relatively limited claims (they aren't trying to assert whole elections are being distorted by Twitter bots) and is indeed higher quality than the ones I've been citing. It actually makes its data available, which is a step forward. But it's had limited impact (28 citations), and it's also not particularly useful. All they're doing is revealing that there is ordinary spam, hijacking and SEO on Turkish Twitter, which was never in doubt. All social media sites have these problems and the authors were tipped off by some amateur third party that highlights these campaigns. Most of what they find is plain commercial spam, there's also some politics in there related to local Turkish issues like cab drivers protesting against Uber but there's no evidence presented that this is actually having a real impact on politics.
The main question here is why are universities spending grant money on subsidizing Twitter? The only people who can do anything with this paper are Twitter's spam team, there isn't generalizable new scientific knowledge coming out of it.
2. Political Astroturfing on Twitter: How to Coordinate a Disinformation Campaign
This one starts with a big claim, so it can at least say it's doing important research. But I really wonder why you suggested it because it actually agrees with us and even destroys the underlying premise of the entire field! A pretty useful paper that might be worth citing in future articles on the topic, in fact.
Firstly, their conclusion is that "if even a powerful and well-financed organization like the South Korean secret service cannot instigate a successful disinformation campaign, then this may be more difficult than often assumed in public debates". In other words, the supposed problem motivating this entire field of >10,000 papers doesn't actually exist: even government agencies fail to have impact when they try to sway opinions with Twitter.
Secondly, they accept that our criticisms of the field are correct. "We argue that past research’s predominant focus on automated accounts, famously known as “social bots” ... misses its target since reports on recent astroturfing campaigns suggest that they are often at least partially run by actual humans" and "Because a ground truth is rarely available, systematic research into astroturfing campaigns is lacking".
They also dunk on ML models on page three, and admit that "these studies still largely focus on anecdotes and lack a theory-driven framework" i.e. are more like blog posts than scientific research. These were all points being made by Gallwitz, Kreil and myself years ago.
The paper does have issues! Still, they should get some cred for being honest about their findings, albeit on the penultimate page of a 25 page study. The first sentence of the paper is phrased in a misleading way: they assert that astroturfing on Twitter has the potential to influence politics, but their conclusion is that it actually doesn't. That's a problem that you see a lot when reading papers in some fields.
Paper 3. QAnon Propaganda on Twitter as Information Warfare.
Note that this paper also isn't about bots. It's a complaint about the behavior of real American people. Where is the actual science? Why are you picking this as an example of high quality research? It's not only blatantly partisan, reading more like a Guardian op-ed than a research paper, it starts by citing paper (2), the one that wrecks the whole premise of the field! They are happy to cite it as evidence that they should look for astroturfing instead of bots, but forget to mention that it shows that even an intelligence agency was unable to have any impact on politics by running Twitter campaigns. Yet that doesn't stop them asserting that their line of research is important due to the "innovative misuse of social media towards undermining democratic processes by promotion of magical thinking".
This sort of problem is rampant in published research. I've seen it so often that a paper cites another paper which directly undermines the conclusions of the first, yet the authors don't address or even mention it. This sort of thing is just deceptive. If they want to cite paper (2) then they need to tackle its conclusion.
The rest of it is just US Democratic Russiagate talking points. Getting into the accuracy of that is a book-sized job and and not about science, so I won't do that here, there are many such debates on the internet.
So that's your three papers. One is OK but not very valuable, one ends up (unintentionally?) wrecking the premise of the other ~10,000+ papers and one isn't even scientific research. It's unclear how they were picked but if these are really the best examples of high quality research from the field then, indeed, who really cares if Musk cuts it all off.
I'll try and find time to look at the papers you cite as high quality later today.
> you conclude from that that all academic use of the Twitter API is garbage, which is nonsensical
"All" no, a vast amount of it, yes. Is it nonsensical? Twitter themselves concluded this exact same thing even before Musk, both in public blog posts and internal emails (see the Twitter Files for examples).
But we don't really need to cite Twitter as an authority here. Just try to answer this question: what mechanisms exist that are stopping bad science outside the field of social bot research, and why have those mechanisms failed within it? It can't be peer review, university hiring committees and so on because those are all existing within social studies as well.
> Your hyperbolic language ...
What language do you think is hyperbolic, exactly, and why?
> Pointing out the scientific flaws of 'tools' like Botometer is wholly valid, but the effort to research and develop tools for bot identification are a response to the fact of systematic information pollution
This is exactly the sort of problem I'm talking about: this justification is circular. We do bad bot research because we know there are bots, we know there are bots because we do bad bot research. If there were actually big problems with social bots then it would be easy to find them and research them; we wouldn't see this situation where basically all papers are seeing patterns in noise.
Botometer is a good example of that. You admit that it's "scientifically flawed" but with respect, that language is not "hyperbolic" enough. It's not merely flawed, it's outright useless. It had an FP rate of 50% when tested against a known human dataset. Yet the Botometer paper has been cited over 900 times now (up from ~700 when I previously wrote about it). When exactly does the rest of the world get to call time on this bad behavior by the academy? These people are changing the opinions of world leaders on the back of misinformation, the exact problem they claim to be fighting.
> It is not the fault of academics if media pundits over-simplify the fruits of their research.
It wasn't media pundits that made academics cite the Botometer paper over 900 times, or write outright deceptive papers like the one I reviewed. The problem here is academia and the institutions need to start taking responsibility for it. Otherwise you're going to get situations like this one: academia will just get cut off from data. People don't have time to try and figure out which little subsections of the academy are following the rules to separate them from the rest.
