Machine learning is too often perceived as a kind of oracle-like power capable of predicting the unknowable and working out of distribution.
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There's no tradeoff here. You have to understand that such classifiers work not because they have a crystal ball, but because they latch onto superficial markers that are statistically correlated with evergreeness or not (e.g. lots of dates = not evergreen).
The difference in ratings between what people could predict today (wrt current articles) and what people would think 20 years from now is not something that a classifier could possibly predict. So nothing is lost.
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At the same time, the classifier trained on old articles would *simply not work* on today's articles. Too many words/concepts would be unknown or redefined, and the style cues it uses would be outdated. It would produce largely garbage output (or at least very biased)
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How do we know concept drift has been that significant since, say, the ‘90s? Particularly in formal news articles where new slang is less common. I can think of particular examples of new/redefined words but don’t know how much this problem would degrade the classifier’s utility
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