TY  - JOUR
A1  - von der Malsburg, Titus Raban
A1  - Poppels, Till
A1  - Levy, Roger P.
T1  - Implicit gender bias in linguistic descriptions for expected events
BT  - the cases of the 2016 United States and 2017 United Kingdom elections
JF  - Psychological Science
N2  - Gender stereotypes influence subjective beliefs about the world, and this is reflected in our use of language. But do gender biases in language transparently reflect subjective beliefs? Or is the process of translating thought to language itself biased? During the 2016 United States (N = 24,863) and 2017 United Kingdom (N = 2,609) electoral campaigns, we compared participants' beliefs about the gender of the next head of government with their use and interpretation of pronouns referring to the next head of government. In the United States, even when the female candidate was expected to win, she pronouns were rarely produced and induced substantial comprehension disruption. In the United Kingdom, where the incumbent female candidate was heavily favored, she pronouns were preferred in production but yielded no comprehension advantage. These and other findings suggest that the language system itself is a source of implicit biases above and beyond previously known biases, such as those measured by the Implicit Association Test.
KW  - language
KW  - psycholinguistics
KW  - event expectations
KW  - reference
KW  - implicit bias
KW  - open data
KW  - open materials
Y1  - 2020
U6  - https://doi.org/10.1177/0956797619890619
SN  - 0956-7976
SN  - 1467-9280
VL  - 31
IS  - 2
SP  - 115
EP  - 128
PB  - Sage
CY  - London
ER  - 
TY  - JOUR
A1  - Brill, Fabio Alexander
A1  - Passuni Pineda, Silvia
A1  - Espichan Cuya, Bruno
A1  - Kreibich, Heidi
T1  - A data-mining approach towards damage modelling for El Nino events in Peru
JF  - Geomatics, natural hazards and risk
N2  - Compound natural hazards likeEl Ninoevents cause high damage to society, which to manage requires reliable risk assessments. Damage modelling is a prerequisite for quantitative risk estimations, yet many procedures still rely on expert knowledge, and empirical studies investigating damage from compound natural hazards hardly exist. A nationwide building survey in Peru after theEl Ninoevent 2017 - which caused intense rainfall, ponding water, flash floods and landslides - enables us to apply data-mining methods for statistical groundwork, using explanatory features generated from remote sensing products and open data. We separate regions of different dominant characteristics through unsupervised clustering, and investigate feature importance rankings for classifying damage via supervised machine learning. Besides the expected effect of precipitation, the classification algorithms select the topographic wetness index as most important feature, especially in low elevation areas. The slope length and steepness factor ranks high for mountains and canyons. Partial dependence plots further hint at amplified vulnerability in rural areas. An example of an empirical damage probability map, developed with a random forest model, is provided to demonstrate the technical feasibility.
KW  - Natural hazard
KW  - damage model
KW  - residential buildings
KW  - data-mining
KW  - remote
KW  - sensing
KW  - open data
Y1  - 2020
U6  - https://doi.org/10.1080/19475705.2020.1818636
SN  - 1947-5705
SN  - 1947-5713
VL  - 11
IS  - 1
SP  - 1966
EP  - 1990
PB  - Routledge, Taylor & Francis Group
CY  - Abingdon
ER  -