I wonder how much the loyalty of the "good old days" was due to a smaller local labor market for skilled jobs.
Let's say that you are a doctor and you live in a town of 20,000 people with one hospital. You are stuck being "loyal" to that hospital unless you move. Now let's say that you live in a metropolis of 2 million people and multiple hospitals. That gives you the option to move between jobs if they become available.
My impression is that cities and towns used to be smaller in the past and commuter culture wasn't as popular as it is now.
I'm ok looking at the past with rose-tinted glasses, but context is also needed to understand the decisions that people made back then.
> Let's say that you are a doctor and you live in a town of 20,000 people with one hospital. You are stuck being "loyal" to that hospital unless you move.
It’s tangential to the main point of your hypothetical, but: in many states that small town doctor’s salary is a multiple of what it would be in an urban or suburban area, and he’s chosen to live there because he’s making a ton of money.
That’s not what I’m talking about. In my state, experienced specialists make millions working at hospitals in rural areas. Think between three and five times what they’d make doing the same work in a city.
By the same hand on the employer side. If the doctor employee is good then you want to keep them if they are the only good doctor in town. Even if they're not great if they are consistent you want to reward them because the replacement would have to be convincing someone out of town to move. But if you live in a large city with tens or hundreds of possible doctors you know your doctors will likely be poached but there are also more where they came from so why bother rewarding loyalty when you don't need to be loyal to your employees?
Let’s say you want to purchase a math tutoring platform. The cost is $30 per student per year. CA schools get about $5 per student per year in lottery funds to purchase instructional materials (shared across all materials). General fund is pretty much all spent on staff/teachers or facilities. One time covid funds are running out. Schools can purchase textbooks at ~$100 per unit and use them for years without subscriptions.
In short, there is no money in public education for these tools.
These platforms look great, but the price is prohibitive. Maybe some basic aid district could pick them up, but those are usually small (I.e., it won’t keep a start up in business).
Note. This is an observation from California. Other states might find education better.
To your second point, I alway go back to this quote: "You can't fix by analysis what you bungled by design" (Light, Singer and Willett, 1990).
If a paper is broken by design, there isn't much to do after the fact. It's just broken.
The problem is that doing a good RCT takes both time and effort, with the huge risk of having null results, which usually results in a desk rejection from most top journals.
So, you either are a top-fund raising researcher who can both fund multiple RCTs and people to support them, or you just try your best with what you have and hope to squeeze a paper out from you did.
Releasing the data won't really help much if the data generating process is flawed. Sure, other people will be able to run different kind of analyses (e.g., jackknife your standard errors instead of just using a robust correction), but I'm not sure how helpful that will be.
A third issue that I have also encountered is that journal editors have an agenda when putting together an issue, which sometimes overwrites the "quality' of the research with "fit" to the issue. This could lead to "lower quality" articles to be published because they fit the (often unspoken) direction of the journal. Most editors see their role as steering the field towards new directions (a sort of a meta service to the field) and sometimes that comes at the expense of the quality of the work.
> Releasing the data won't really help much if the data generating process is flawed. Sure, other people will be able to run different kind of analyses (e.g., jackknife your standard errors instead of just using a robust correction), but I'm not sure how helpful that will be.
It allows motivated people to catch more subtle forms of nonsense. Data colada, for example, has caught outright fraud, but only through herculean efforts. Imagine what groups like this might do if they had the raw data.
A citation like the one above should normally point to a full reference in a bibliography section. Did you forget the \bibliography{} command at the end of your comment?
> It always irks me that discussion of space debris invariably talks about their high velocity relative to the ground, which is completely meaningless.
It's obviously an issue with point of view, but considering only relative velocity isn't also a solution.
If two spacecrafts are on a close approach trajectory, one of them might need to course correct to fix that. Changing the relative direction might allow them to avoid the collision, but will also change their orbit, leading to other problems down the road (e.g., out of wack apo/periapsis). Fixing the orbit after the close approach will also cost more fuel and/or angular momentum, which is in short supply on most spacecrafts.
I like to think about it like two nascar cars. Sure, if you are driving one, the other cars seem to be almost not moving from your seat, but the spectators see something completely different. Any small change (from the driver's view) in steering can have big effects overall.
Probably not. A pasta tube is the easiest shape to make by extrusion (and difficult to make by hand) and it was likely the first thing they tried after thinking of using extrusion to make pasta.
Ish. Yes, the basic shapes come from what was possible back then, but some varieties ended up being more popular because they do work better. Sadly I lack the English vocabulary to describe the difference between penne liscie and penne rigate...
This is what ChatGPT has to say about the difference. Does it capture your view of the matter?
Penne liscie and penne rigate are both types of penne pasta, but they differ in their surface texture:
1. *Penne Liscie*: This type of penne has a smooth surface. The word "liscie" in Italian means "smooth." Due to its smooth texture, it generally holds lighter sauces better, as the surface doesn't trap as much sauce.
2. *Penne Rigate*: In contrast, penne rigate has a ridged surface. "Rigate" means "ridged" or "grooved" in Italian. These ridges or grooves are excellent for holding onto thicker, heartier sauces, as they provide more surface area for the sauce to adhere to.
The choice between the two usually depends on the type of sauce being used and personal preference. Penne rigate's ability to hold more sauce makes it a popular choice for many robust and creamy sauces.
It looks like pasta shapes are patentable [1], but I wasn't able to find a patent for this specific pasta shape on Google Patents.
On the podcast, they talk more about the process of creating the cutting die and how much that costs, etc. Also, they had some trouble finding a pasta mill to make the exact shape they wanted. So, even if it's not patented, it might not be profitable to just copy it. After all, most other shapes are cheaper and no "real" pasta company is going to get into a PR fiasco just to sell a different shape out of the 100s they already have.
My biggest concern is that this will allow people to type a question and get a number back from the database, without being able to tell if the query is right or if the LLM just made up something.
It can work to support business analysts to crank out more reporters, but I wouldn’t roll it out to all my staff.
I've actually done exactly what @qsort suggested and outputted the intermediate SQL query and raw data generated by that query when generating the response back to the user. That definitely helps in establishing more trust with the customer since they can verify the response. My approach right now is to just be honest with our customers in the capabilities of the tool, acknowledge its shortcomings, and keep iterating over time to make it better and better. That's what the team in charge of our company-wide custom LLM has done and it's gained a surprising amount of traction and trust over the last few months.
The idea is to coil the sewage pipe with the cold water pipe before it goes into the water heater. That way the cold water will pick up some of the excess heat from the sewage pipe from the shower and it will cost less to heat overall.
Maybe a better descriptor would be that peer review is a reputation-based system.
The peers that will review your work likely know about the paper you submitted already, because they work on related work themselves and sat through your conference presentations. Most of them want you to publish your work and will provide a good/non-adversarial review of a paper.
Sometimes though, your paper hits too close to home for them, then they will try not to get it published or will slow walk the review so that their own work can come out before yours or at the same time.
On top of that, you have journal editors who can see everything about the process and can decide to ignore a good/bad review to fit their ideas about the paper itself and to fit the overall vision they have for the journal for the coming publication schedule.
> Sometimes though, your paper hits too close to home for them, then they will try not to get it published or will slow walk the review so that their own work can come out before yours or at the same time.
How often does this actually happen? Can't say I've heard of people doing this.
Oof, the paper is tough to read. The default word formatting is not the best for tables.
I am wondering how marriage/relationship plays a role into career choices. I believe that people from privileged accounts are more likely to be in a stable relationship, which could (at least in part) explain why one partner can pursue a low-paying high-prestige profession while the other pursues a high-paying one.
When at least one is from privileged background they usually can both work lower paying jobs.
It's a lot easier to make a decent living out of it if you already own a house and your parents can act as a safety net in case unforseen huge expenses fall from the sky (e.g. if your parents total their car: you won't need to cover for them)
Let's say that you are a doctor and you live in a town of 20,000 people with one hospital. You are stuck being "loyal" to that hospital unless you move. Now let's say that you live in a metropolis of 2 million people and multiple hospitals. That gives you the option to move between jobs if they become available.
My impression is that cities and towns used to be smaller in the past and commuter culture wasn't as popular as it is now.
I'm ok looking at the past with rose-tinted glasses, but context is also needed to understand the decisions that people made back then.