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Affect labeling is an implicit emotional regulation strategy that can be simply described as "putting feelings into words". Specifically, it refers to the idea that explicitly labeling one's, typically negative, emotional state results in a reduction of the conscious experience, physiological response, and/or behavior resulting from that emotional state. [1]
label description Copy Editing rephrase; improve grammar, spelling, tone, or punctuation. Clarification specify or explain an existing fact or meaning by example or discussion without adding new information. Simplification reduce the complexity or breadth of discussion; may remove information. Point of View
Labelling or using a label is describing someone or something in a word or short phrase. [1] For example, the label "criminal" may be used to describe someone who has broken a law. Labelling theory is a theory in sociology which ascribes labelling of people to control and identification of deviant behaviour.
Affect control theory's computer program predicts the plausible re-identifications, thereby providing a formal model for labeling theory. The sentiment associated with an identity can change to befit the kinds of events in which that identity is involved, when situations keep arising where the identity is deflected in the same way, especially ...
Labeling theory holds that deviance is not inherent in an act, but instead focuses on the tendency of majorities to negatively label minorities or those seen as deviant from standard cultural norms. [ 1 ] [ 2 ] The theory was prominent during the 1960s and 1970s, and some modified versions of the theory have developed and are still currently ...
Labels logo. Our goal in this project is to produce labeled datasets for pressing needs of the Wikipedia community. Labeled datasets have a variety of uses including research (e.g. qualitative analyses of newcomer quality [1] and editor interactions [2]) and the development of advance wiki tools (e.g. the models used by User:ClueBot NG and WP:STiki).
In order to perform difficult analyses and train intelligent wiki-tools (e.g. for detecting vandalism and assessing the quality of articles), we need labeled data and lots of it.
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