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JollyG

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JollyG ,

This article is a very good example of why current media is terrible.

This article is a summary of someone else’s work. It does not contain any news. Literally. It contains no new information, no original reporting, and adds nothing to the understanding of the situation in Florida one may glean from reading the CNN article the New Republic is ripping off. What is does is take the reporting done by CNN, which was far more even-keeled, and dresses it up in more incendiary language to outrage media consumers who want information that is consistent with what they already believe.

If you didn’t read the CNN article, this is what it did: A reporter at CNN interviewed several lawyers who had cases before Cannon. Those lawyers were asked what they thought about the judge and offered the following opinions:

  1. She is very detail oriented
  2. She is rigid and provincial when it comes to procedure and local rules.
  3. She is indecisive.
  4. She sometimes seems overwhelmed.
  5. She focuses on abstract issues, or otherwise obsesses over elements of the case that seem irrelevant to trial lawyers rather than making decisions about factual questions.
  6. She is not going to defer to the prosecutor automatically, even in situations where the defense and prosecutor agree.
  7. One lawyer felt she was harsh towards defendants in general but was less harsh towards Trump in this particular case.

The CNN article suggests that a a combination of some or all of factors 1-7 have made it easy for the defense in the Trump case to gum up the works and slow the progress of the trial down.

Most of these opinions are fairly anodyne. Many of them could describe almost any federal judge. Some of them even seem like good characteristics for a federal judge. (I think it is good, for example, that a federal judge requires prosecutors to back up their assertions and motions with specificity, rather than try to justify motions with generic claims.) Whats more is that none of these opinions would be particularly surprising to anyone who has been following the news surrounding Trump’s Florida trial. Nothing in the CNN reporting is particularly “damning” as the New Republic characterizes the report. The New Republic focuses on the strongest criticism of Cannon, but that criticism is the opinion of a single lawyer, and only represented a small portion of the overall report offered by CNN. If you only read the New Republic’s version, you would be forgiven for thinking that was the focus of the CNN article. In that case you would have an inaccurate view of the article, which is itself mostly a summary of opinions. I will also note that, when the New Republic was copying CNN’s homework, they ignored the praise defense lawyers had for Cannon. But I suppose if they had included the praise it would have been harder to call the article “damning”.

To put it plainly, the New Republic article is trash. It is a summary of someone else’s reporting that hypes up the most negative opinion about a federal judge, while ignoring the bulk of the same reporting.

JollyG ,

Unhelpful Linux User Archetypes:

The Configurator: All problems are configuration problems. The fact that a user has a problem means they configured their machine incorrectly. All help requests are an opportunity to lecture others about configuration files.

The lumberjack: Insists on logs no matter how simple or basic the question. “How do I get the working directory in the terminal?” -Sorry, I can’t help you unless you post your log. “What does the -r flag do?” -You need to post a log for me to answer that question. “Is there a way to make this service start at boot?” -We have no way of knowing unless you post your log. When a user posts their log, the lumberjack’s work is done. No need to reply to the thread any further.

The Anacdata Troubleshooter: Failed to develop a theory of mind during childhood. Thinks their machine is representative of all machines. If they don’t have an issue, the user is lying about the issue.

The Jargon Master: Uses as much jargon as possible in forum posts. If a user doesn’t know each and every term, that’s on them. If you did not commit to mastering every aspect of a piece of software before asking for help, were you even trying to solve the problem?

The Hobby Horse Jockey: All problems are caused by whatever thing the contributor does not like. Graphics driver issue? Snaps. Computer won’t post? Obviously, Snaps. Machine getting too hot? Snaps. Command ‘flatpack’ not found? Oh you better believe snaps did that.

The Pedantfile: Gets mad because everyone asks their questions the wrong way. Writes a message letting the user know they asked their question wrong. Message usually appears within a minute or two of someone providing a solution to the user.

JollyG ,

Thanks for reaffirming my bias that new cars suck.

I am really concerned the next car I need to buy, which is probably 20 years off, is going to be this trend cranked to 11. With software and hardware I can find alternatives and hack my way around the “you paid for it, but we own it and can do whatever we want to it” mentality that tech companies push, but cars seem like a whole different world when it comes to the “you paid for it, but we own it” mentality.

JollyG ,

Or you only consider your expenses after savings and think that you are “living paycheck to paycheck” because you use up all your non-invested money by the end of the month.

JollyG ,

This really isn’t a study, so much as a lit review. Sort of. Anyway, in the fully remote section they cite three studies that argue show a fall in productivity. The first (Emmanuel and Harrington (2023)) found an 8% drop in call volume as a call center shifted to fully remote work at the onset of the pandemic. But their comparison group was a group of call center employees who were always remote. So even if you buy the argument that the change call volume is solely attributable to a drop in productivity, you cannot conclude that the productivity shift was caused by working from home, the group that shifted from on-location to remote work did 8% worse than the group than the always remote work!

The second study (Gibbs, Mengel and Siemroth (2022)) is, again, an analysis of call-center employees (this time in India) who shifted to remote work at the onset of the pandemic. They find no change in productivity, but that employees are working longer hours at home, which they argue means a real 8-19% drop in productivity.

The final study (Atkin, Schoar, and Shinde (2023)) is another firm from India which involved a randomized controlled study which finds an 18% drop in productivity for data entry work.

So, just taking their lit review at face value, one of their studies directly contradicts their argument, yet they somehow present it as if it is evidence of a causal relationship between working from home and productivity. Another study shows no effect, so they break out some razamataz math to try to turn no effect into a negative effect. Only one of the three studies shows a plausible effect.

Since these are the only three papers they cite to support their argument that fully remote work causes a drop in firm productivity, let’s look at them in more depth.

If you go to their references section, you find that there is not a Emmanuel and Harrington (2023) cited. Hey, that a bad sign. There is an Emmanuel and Harrington 2021, but its an unpublished paper. Maybe it got published and they just forgot to update the cite? I plugged the title into google scholar, and find one result, with no copy of the working paper, and no evidence of any sort of publication record from any journal. Plugging the title into regular google returns a “Staff Report” of the federal reserve bank of NY. So not a peer reviewed article. They employ whats known as a difference-in-difference design to compare employees who shifted from fully in person to fully remote. They report a 4% reduction in productivity for these workers, not the 8% reported in the original article. I just skimmed the article, so maybe they get their 8% figure someplace else. What is interesting to me though is that their DID models seem to show there is not any difference between the different groups for most of the periods of observation. IDK. I’d have to read more in-depth to make up my mind.

It seems like these conclusions, whatever you make of them should really only be applied to call-center work during the pandemic.

JollyG ,

That is really just a map of poverty.

https://lemmy.world/pictrs/image/bfeef262-a3a7-4567-8535-14cd2cc1f629.png

JollyG ,

Meh, its not a perfect correlation (and the time series for the poverty map and the diabetes map are different), but most chronic diseases tend correlate with poverty pretty well. You should look at a map of obesity. It follows the same form.

JollyG ,

That seems like good news for Mastodon

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