Those options seem fine for a poll imo. If you ask the same question to older demographics and more people pick “enemy”, then isn’t the conclusion in the headline valid?
I know what you’re saying, but it’s still a shitty poll. I think people in the past were way looser with the word enemy. Everyone was an enemy, the Russians, communism, drugs, immigrants poverty… everything was a fucking enemy that needed a war.
So, even though just as many people might distrust China the language has changed and we wouldn’t call them “enemy”.
The Chinese government is authoritarian, evil and awful but I still wouldn’t call China an “enemy”. Because life isn’t black and white, and once you call somone an enemy you’ve shut off your brain and nothing good will come out of it.
A lot of nuance will be missed without some gradation between “I <3 China” and “Down with Pooh!” For example, if we added “Slightly favorable”, “Neutral”, and “Slightly unfavorable” we would begin to see just how favorable younger generations are. Rather than presume there is a deep divide on trade policy, if two bars are almost equal, we may see they are largely neutral. Similarly we could see just how favorable their views of TikTok really are by looking at the spread between neutral to “I <3 China!”
The issue is that your reducing a multivariable spectra to a single binary. That kind of data compression destroys a massive amount of valuable data, and alot of nuance along with it.
Those options seem fine for a poll imo. If you ask the same question to older demographics and more people pick “enemy”, then isn’t the conclusion in the headline valid?
I know what you’re saying, but it’s still a shitty poll. I think people in the past were way looser with the word enemy. Everyone was an enemy, the Russians, communism, drugs, immigrants poverty… everything was a fucking enemy that needed a war.
So, even though just as many people might distrust China the language has changed and we wouldn’t call them “enemy”.
The Chinese government is authoritarian, evil and awful but I still wouldn’t call China an “enemy”. Because life isn’t black and white, and once you call somone an enemy you’ve shut off your brain and nothing good will come out of it.
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A lot of nuance will be missed without some gradation between “I <3 China” and “Down with Pooh!” For example, if we added “Slightly favorable”, “Neutral”, and “Slightly unfavorable” we would begin to see just how favorable younger generations are. Rather than presume there is a deep divide on trade policy, if two bars are almost equal, we may see they are largely neutral. Similarly we could see just how favorable their views of TikTok really are by looking at the spread between neutral to “I <3 China!”
The issue is that your reducing a multivariable spectra to a single binary. That kind of data compression destroys a massive amount of valuable data, and alot of nuance along with it.