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Fairlex: A multilingual benchmark for evaluating fairness in legal text processing ...
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Fairlex: A multilingual benchmark for evaluating fairness in legal text processing ...
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FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing ...
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One Country, 700+ Languages: NLP Challenges for Underrepresented Languages and Dialects in Indonesia ...
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Introducing Neural Bag of Whole-Words with ColBERTer: Contextualized Late Interactions using Enhanced Reduction ...
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Coloring the Blank Slate: Pre-training Imparts a Hierarchical Inductive Bias to Sequence-to-sequence Models ...
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YET LIE THEY DO: A LIST-EXPERIMENT FOR ESTIMATING ANTI-IMMIGRANT SENTIMENT AND SOCIAL DESIRABILITY BIAS
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Abstract:
The measurement of anti-immigrant sentiment (AIS) is treacherous. Manifestations of animosity, or even misgivings, might be associated with racism and xenophobia. Thus, survey respondents with unwelcoming attitudes may make incorrect statements when asked about their feelings toward immigrants, or indeed other attitude facets regarding international migration. To reduce such measurement error (commonly labelled ¿social desirability bias¿, SDB), survey methodologists have developed various anonymity-maximizing techniques. One such is the list-experiment, also known as item-count-technique (ICT), which consists of dividing the sample in treatment and control arms with a view to comparing the mean scores obtained when asking about the number of items that elicit a specific attitude or assessment. The item of interest is included only in the treatment group¿s list. Since no request is made to identify the relevant items, just the number, the method assumes that respondents will answer truthfully. Comparison with the percentage obtained by a direct question on the same sensitive item is supposed to reveal the scope of SDB. This paper reports on a list-experiment on AIS fielded in Spain in the framework of a study on immigration attitudes financed by the European Fund for Regional Cohesion and the Spanish Ministry of Science (project ref. CSO2017-87364-R). Contrary to expectations, aggregate ICT-based estimates of AIS almost coincide with those based on the DQ. At face value, this result suggests the absence of social desirability pressures. However, hidden beneath those aggregate values lurks evidence of SDB substantial enough to question the list-experiment¿s ¿no liars¿ assumption.
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URL: http://hdl.handle.net/10261/254285
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Modified Gravity and Cosmology: An Update by the CANTATA Network
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In: https://hal.archives-ouvertes.fr/hal-03261155 ; 2021 (2021)
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The Dawn of the Human-Machine Era: A forecast of new and emerging language technologies.
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In: https://hal.archives-ouvertes.fr/hal-03230287 ; 2021 (2021)
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UNITEX 3.3 User Manual
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In: https://hal.archives-ouvertes.fr/hal-03589580 ; 2021 (2021)
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UNITEX 3.3 Manuel d'utilisation
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In: https://hal.archives-ouvertes.fr/hal-03589598 ; 2021 (2021)
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Modified Gravity and Cosmology: An Update by the CANTATA Network ...
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UnibucKernel: Geolocating Swiss German Jodels Using Ensemble Learning ...
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Towards Human-Free Automatic Quality Evaluation of German Summarization ...
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Linguistically Informed Masking for Representation Learning in the Patent Domain ...
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Persuasive Natural Language Generation -- A Literature Review ...
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