Results 21 to 30 of about 114,823 (232)
Model selection for mixture‐based clustering for ordinal data
, 2016 One of the key questions in the use of mixture models concerns the choice of the number of components most suitable for a given data set. In this paper we investigate answers to this problem in the context of likelihood-based clustering of the rows of a ...
D. Fernández, R. Arnold
semanticscholar +2 more sources
A Robust Bias Mitigation Procedure Based on the Stereotype Content Model [PDF]
NLPCSS, 2022 The Stereotype Content model (SCM) states that we tend to perceive minority groups as cold, incompetent or both. In this paper we adapt existing work to demonstrate that the Stereotype Content model holds for contextualised word embeddings, then use ...
Eddie L. Ungless +3 more
semanticscholar +1 more source
Understanding and Countering Stereotypes: A Computational Approach to the Stereotype Content Model [PDF]
Annual Meeting of the Association for Computational Linguistics, 2021 Stereotypical language expresses widely-held beliefs about different social categories. Many stereotypes are overtly negative, while others may appear positive on the surface, but still lead to negative consequences.
K. Fraser +2 more
semanticscholar +1 more source
British Educational Research Journal, EarlyView., 2023 Abstract Early childhood has increasingly been acknowledged as a vital time for all children. Inclusive and quality education is part of the United Nations Sustainable Development Goals, with the further specification that all children have access to quality pre‐primary education.
Laura H. V. Wright +8 more
wiley +1 more source
Social-Group-Agnostic Bias Mitigation via the Stereotype Content Model
Annual Meeting of the Association for Computational Linguistics, 2023 Existing bias mitigation methods require social-group-specific word pairs (e.g., “man” – “woman”) for each social attribute (e.g., gender), restricting the bias mitigation to only one specified social attribute.
Ali Omrani +7 more
semanticscholar +1 more source
Workshop on Speech and Language Processing for Assistive Technologies, 2022 Stereotypes are a positive or negative, generalized, and often widely shared belief about the attributes of certain groups of people, such as people with sensory disabilities. If stereotypes manifest in assistive technologies used by deaf or blind people,
Brienna Herold +2 more
semanticscholar +1 more source
Overcoming constraints of the model minority stereotype to advance Asian American health.
American Psychologist, 2021 Asian Americans are the fastest growing U.S. immigrant group, projected to become the largest immigrant group by 2065, but the quantity of research on Asian Americans' health has not mirrored changing demographics.
Jacqueline H. J. Kim +2 more
semanticscholar +1 more source
, 2012 The stereotype that women are dependent on men is a commonly verbalized, potentially damaging aspect of benevolent sexism. We investigated how women may use behavioral disconfirmation of the personal applicability of the stereotype to negotiate such ...
Wakefield, Juliet R.H. +5 more
core +1 more source
Respondent behavior and data quality aspects in panel surveys : four empirical contributions [PDF]
, 2006 The four essays of this dissertation provide a number of new and unique insights on diverse issues of respondent behavior and aspects of data quality in surveys.
Serfling, Oliver
core +1 more source
The Tourist Stereotype Model: Positive and Negative Dimensions
Journal of Travel Research, 2020 This research proposes a measurement model to evaluate tourist stereotypes. Study 1 assesses the positive and negative tourist stereotypes that Hong Kong residents hold toward Chinese outbound tourists by connecting previous research on stereotypes from ...
V. Tung, B. King, S. Tse
semanticscholar +1 more source