> The document said that improving racial and gender diversity is less important than making sure conservatives feel comfortable expressing themselves at work.
I'm all for skill over diversity, but "making conservatives comfortable" seems like a weird thing to write. But until the document comes out it will be difficult to judge it, and hearsay and "shaking people" are not a good measure of it's content.
Yeah, it was on the front page for a bit but got (I assume) flagged off.
Which, for once, I agree with. Sooner or later the document will presumably come out, but until that happens I seriously doubt any useful conversations can be had about a manifesto that nobody's read.
If this was someone left wing who published a piece saying that women should be paid more then it would hardly cause any commotion. I haven't read the document but it seems that those that disagree with the contents now want to get the author fired. Which seems to have been part of the authors point, it is a safe space for people if they are ideologically left wing bit not if you are from the other end of the political spectrum. I consider myself left wing but echo chambers help no one.
How about people just stop talking about politics at work? If it isn't germane to your job and isn't pleasantries like talking about your weekend or your hobbies or moving or your kids or whatever, just leave it at home. It's just like dating coworkers: sure, it might not be expressly forbidden by office policy, but does that mean it's a good idea?
In general I agree, nothing good ever came from discussing religion or politics in a social setting. However there is a tie t the environment in this instance, it is about the quality of work and the remuneration for said work, that does impact on the people involved. If everyone gets paid the same for doing the same job but X produces measurably more work, then there is a disincentive for X to achieve highly if Z gets rewarded inline with the work done by X.
In development this may not be as easily measured but, for example, in fruit picking I worked alongside my girlfriend and at the end of day 1 I was asked if I would come back of my girlfriend was not asked to come back. I produced, she didn't and because she could not do as much she actually held back everyone else (not just her, a few mainly females, were not offered another day's work. We were all getting paid a daily rate, so there was a disincentive when someone who could not do as much was getting paid the same. As I say that is in a situation where output can be easily measured and compared, I'm sure there are metrics in development that allow for similar comparisons but can't be certain.
I think they're saying the author should be fired because they're openly creating a hostile workplace for other groups.
And historically, women are underrepresented in tech, and are not in a position of power. Conservative men are. One of the reasons why stuff like this gets such a much more negative response is because it's seen as "punching down."
It should be trivial to measure performance these days and figure out if there indeed is/isn't any biological difference or if certain parts of DNA/cultural background etc. are the decisive factors. It's Google anyway, "The ML company". They should internally test this hypothesis and then publish their findings (hopefully they can do objective studies with high integrity). If diversity programs cause them become less competitive, they will wither at some point; if diversity helps them they will earn economical success (unless all companies become alike). They have a perfect playground internally for that.
And what if people don't like the result? If the result will be that there is a biological difference between the sexes will people accept the result or just start calling google a sexist and bigoted company and then google will suffer? There is a Norwegian documentary about biological differences but that didn't change anything.
Let's wait until some research is done. It's premature to say what the real result would be even if we can argue whether pure primitive biology-based behaviorism/evolutionary psychology will be more important than more gentle approaches the civilization is built upon. Maybe we will even gain an insight where the breaking points are.
You have so many metrics these days as well as a complete view of the bottom-line. Google's ML is amongst the best, they have possibilities most people don't even get a hint of.
It's very much not trivial, though. Given two solutions to a coding problem, how do you objectively determine which one is better? More importantly, how do you objectively tell if either of them is good enough? Or if they're poor?
Probably wrong granularity; we would likely have to wait a bit for this detailed ML. But you can make various groups that follow certain "ideology" and see which ones thrive under what circumstances/in what kind of environment or not. Kinda like A/B testing but internally on their own coders. Then you can view e.g. maintenance costs (that might be your code quality metrics aggregated over time) etc. If there is one company that can pull this off, it's Google. I would be very interested in results to be honest. Then this could be generalized for governments and provide fact-based guidelines on how to govern well.
Unfortunately, how do you control that both groups are the same except for the metric you're trying to measure? And, once again, how do you accurately measure maintenance costs, especially over different projects that would likely have variations in requirements? And, most importantly, how do you come up with objective measurements for these things?
First, it's going to be an approximation and would take decades to carry out, as we humans are slow and no GPU/cloud would accelerate us :-( Second, the idea I had on mind was to use ML to figure out which signals/features ire the most important ones; just dump all raw data onto it and expect it would figure it out. Kinda like what you see from PCA which gives you base vectors guaranteeing some correlation properties, or CNNs where you expects neural network to come up with convolutions it needs on its own.
With maintenance costs I'd probably record as much data as I can and then try to find in a given case what kinds of signals were present. If I see some are repeating and are beneficiary or not, I can have some classifier from them. Also, it could unearth parasitistic relations when certain group of individuals flourishes only because they take advantage of certain individuals with different ethics, but never thrive in other kinds of environments.
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[ 4.0 ms ] story [ 45.9 ms ] threadI'm all for skill over diversity, but "making conservatives comfortable" seems like a weird thing to write. But until the document comes out it will be difficult to judge it, and hearsay and "shaking people" are not a good measure of it's content.
Which, for once, I agree with. Sooner or later the document will presumably come out, but until that happens I seriously doubt any useful conversations can be had about a manifesto that nobody's read.
If someone else is talking toxic politics at work, and you don't contest them, you get their politics.
In development this may not be as easily measured but, for example, in fruit picking I worked alongside my girlfriend and at the end of day 1 I was asked if I would come back of my girlfriend was not asked to come back. I produced, she didn't and because she could not do as much she actually held back everyone else (not just her, a few mainly females, were not offered another day's work. We were all getting paid a daily rate, so there was a disincentive when someone who could not do as much was getting paid the same. As I say that is in a situation where output can be easily measured and compared, I'm sure there are metrics in development that allow for similar comparisons but can't be certain.
And historically, women are underrepresented in tech, and are not in a position of power. Conservative men are. One of the reasons why stuff like this gets such a much more negative response is because it's seen as "punching down."
With maintenance costs I'd probably record as much data as I can and then try to find in a given case what kinds of signals were present. If I see some are repeating and are beneficiary or not, I can have some classifier from them. Also, it could unearth parasitistic relations when certain group of individuals flourishes only because they take advantage of certain individuals with different ethics, but never thrive in other kinds of environments.