#classification scheme

[daily-wikidata]

[floofshy]

a classification of ways parent's hurt their children

involuntary - the parent doesn't want to hurt the child but lacks the skills and resources to give them a good childhood. For example, or a parent who accidentally had a child and kinda resents them, and doesn't have good enough acting skills to keep the child from noticing.

selfishness - the parent chooses to favour their own desires at the expense of the child explicitly. Like punishing children for crying because the parent doesn't want to hear the sobs.

mistaken - the parent thinks they are helping the child, but the parent is mistaken about the state of the world and is actually harming the child. I think this is perhaps the most common way to hurt children, as parents are willing to do all sorts of things to their children for the mere chance of a future pay off. Like forcing their child to take music lessons which the child hates so that the child is more well rounded. Or denying a child the right to watch a TV show because the parent heard something bad about it. This category more severe ways of hurting children like anti-vax or conversion therapy. of course, this classification scheme has edge cases, like a parent who requires too much safety of their child (like doesn't let the child play outside so they won't get kidnapped). That is both mistaken and selfishness; kidnapping isn't that common and the parent is over valuing their fear at losing a child compared to the child's desire for fun. (Parents tend to systematically undervalue a child's happiness compared to a child's safety)

[englischdeutschubersetzung]

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[rate-lower-exchange-ynn]

The Way the Type as respects OPM Classification Program Career?

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[transformativetidbits]

DEviaNT: Double Entendre Via Noun Transfer

Abstract: Humor identification is a hard natural language understanding problem. We identify a subproblem--the "that's what she said" problem--with two distinguishing characteristics: 1) use of nouns that are euphemisms for sexually explicit nouns, and 2) structure common in the erotic domain. We address this problem in a classification approach that includes features that model those two characteristics. Experiments on web data demonstrate that our approach improves precision by 12% over baseline techniques that use only word-based features.

"That's What She Said: Double Entendre Identification"

Chloe Kiddon & Yuriy Brun

Computer Science and Engineering, University of Washington (Seattle)