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21
Dataset for paper "Anticipating causes and consequences" currently under review - Experiment 2 Very Early Data with Differences ...
Garnham, Alan. - : University of Sussex, 2020
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22
Norms for the paper "Implicit Consequentiality in English: A Corpus of 300+ Verbs" ...
Garnham, Alan; Kaplanova, Karolina; Vorthmann, Svenja. - : University of Sussex, 2020
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23
Dataset for paper "Anticipating causes and consequences" - Experiment 2 Very Early Data with Differences ...
Garnham, Alan. - : University of Sussex, 2020
BASE
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24
Analysis script for paper "Anticipating causes and consequences" - Example R Analysis for Experiment 1 Very Early Effect - Differences ...
Garnham, Alan. - : University of Sussex, 2020
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25
Dataset for paper "Anticipating causes and consequences" - Experiment 1 Early Effect Data with Differences ...
Garnham, Alan. - : University of Sussex, 2020
BASE
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26
Analysis script for paper "Anticipating causes and consequences" currently under review - Example R Analysis for Experiment 1 Very Early Effect - Differences ...
Garnham, Alan. - : University of Sussex, 2020
BASE
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27
Dataset for paper "Anticipating causes and consequences" currently under review - Experiment 1 Early Effect Data with Differences ...
Garnham, Alan. - : University of Sussex, 2020
BASE
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28
Dataset for paper "Anticipating causes and consequences" Experiment 1 Very Early Effect Data with Differences ...
Garnham, Alan. - : University of Sussex, 2020
BASE
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29
Dataset for paper "Anticipating causes and consequences" - Experiment 1 Early Effect Data with Differences ...
Garnham, Alan. - : University of Sussex, 2020
BASE
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30
Research data for paper "Anticipanting causes and consequences" ...
Garnham, Alan; Hutton, Sam; Child, Scarlett. - : University of Sussex, 2020
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31
Dataset for paper "Anticipating causes and consequences" - Experiment 2 Late Effect Data with Differences ...
Garnham, Alan. - : University of Sussex, 2020
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32
Research Data for Paper "Implicit Consequentiality Bias in English: A Corpus of 300+ Verbs" ...
Garnham, Alan; Vorthmann, Svenja; Kaplanova, Karolina. - : University of Sussex, 2020
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33
ggplot2 scripts for figures in the paper "Implicit Consequentiality Bias in English: A Corpus of 300+ Verbs" ...
Garnham, Alan; Vorthmann, Svenja; Kaplanova, Karolina. - : University of Sussex, 2020
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34
Research Data for Paper "Implicit Consequentiality Bias in English: A Corpus of 300+ Verbs" ...
Garnham, Alan; Vorthmann, Svenja; Kaplanova, Karolina. - : University of Sussex, 2020
BASE
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35
ggplot2 scripts for figures in the paper "Implicit Consequentiality Bias in English: A Corpus of 300+ Verbs" ...
Garnham, Alan; Vorthmann, Svenja; Kaplanova, Karolina. - : University of Sussex, 2020
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36
Implicit consequentiality bias in English: A corpus of 300+ verbs
In: Behav Res Methods (2020)
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37
Implicit consequentiality bias in English: a corpus of 300+ verbs
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38
Physiological and perceptual correlates of masculinity in children’s voices
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39
“This is what a mechanic sounds like.” Children’s vocal control reveals implicit occupational stereotypes
Cartei, Valentina; Oakhill, Jane; Garnham, Alan. - : SAGE Publications, 2020
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40
A Language Index of Grammatical Gender Dimensions to Study the Impact of Grammatical Gender on the Way We Perceive Women and Men. ...
Abstract: Psycholinguistic investigations of the way readers and speakers perceive gender have shown several biases associated with how gender is linguistically realized in language. Although such variations across languages offer interesting grounds for legitimate cross-linguistic comparisons, pertinent characteristics of grammatical systems - especially in terms of their gender asymmetries - have to be clearly identified. In this paper, we present a language index for researchers interested in the effect of grammatical gender on the mental representations of women and men. Our index is based on five main language groups (i.e., grammatical gender languages, languages with a combination of grammatical gender and natural gender, natural gender languages, genderless languages with few traces of grammatical gender and genderless languages) and three sets of specific features (morphology, masculine-male generics and asymmetries). Our index goes beyond existing ones in that it provides specific dimensions relevant to those ...
Keyword: 300 Social sciences, sociology & anthropology; 440 French & related languages; 840 French & related literatures
URL: https://boris.unibe.ch/139858/
https://dx.doi.org/10.7892/boris.139858
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