Kenji or Kenneth? Pearl Harbor and Japanese-American assimilation

Saavedra, Martin

doi:10.1016/j.jebo.2021.03.014

Cited by 16 publications

(5 citation statements)

References 39 publications

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“…This might be in the form of geopolitical tension between a country where the language of the model is predominantly spoken and a country that we consider as a nationality. This is consistent with the evidence in the literature that geopolitical tensions stoke discrim-inatory attitudes (Saavedra, 2021). For example, tensions between the US and countries like Iran and China are likely driving lower scores for Iranian and Chinese in the English PLM (Lee, 2022;Sadeghi, 2016).…”

Section: Linkage To Real-world Biasessupporting

confidence: 90%

Language-Agnostic Bias Detection in Language Models with Bias Probing

Köksal,

Yalcin,

Akbiyik

et al. 2023

Findings of the Association for Computational Linguistics: EMNLP 2023

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Pretrained language models (PLMs) are key components in NLP, but they contain strong social biases. Quantifying these biases is challenging because current methods focusing on fill-the-mask objectives are sensitive to slight changes in input. To address this, we propose a bias probing technique called LABDet, for evaluating social bias in PLMs with a robust and language-agnostic method. For nationality as a case study, we show that LABDet "surfaces" nationality bias by training a classifier on top of a frozen PLM on non-nationality sentiment detection. We find consistent patterns of nationality bias across monolingual PLMs in six languages that align with historical and political context. We also show for English BERT that bias surfaced by LABDet correlates well with bias in the pretraining data; thus, our work is one of the few studies that directly links pretraining data to PLM behavior. Finally, we verify LABDet's reliability and applicability to different templates and languages through an extensive set of robustness checks. We publicly share our code and dataset in https://github.com/akoksal/LABDet.

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Section: Linkage To Real-world Biasessupporting

confidence: 90%

Language-Agnostic Bias Detection in Language Models with Bias Probing

Köksal,

Yalcin,

Akbiyik

et al. 2023

Findings of the Association for Computational Linguistics: EMNLP 2023

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“…Also, Black enlistment from places with 44 See Soennichsen (2011) for a detailed discussion. Saavedra (2021) shows that Japanese-Americans born right after Pearl Harbor had more American sounding names, relative to kids born just a few days before, as Japanese-American parents responded to concerns about heightened anti-Japanese sentiments. low discrimination increased dramatically after the U.S. government embarked on a campaign to recruit Black men, even though there were few substantive changes to racial discrimination in the Army or U.S. society.…”

Section: Discussionmentioning

confidence: 99%

Racial Discrimination and the Social Contract: Evidence from U.S. Army Enlistment during WWII

Qian¹,

Tabellini²

2021

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for excellent research assistance. All mistakes are our own. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

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“…A second possible choice is to assimilate. Assimilation can take different forms: adopting natives' cultural norms (Jaschke et al, 2022), changing one's name or one's children's names (Fouka, 2019;Saavedra, 2021), marrying a native (Gould and Klor, 2016), or naturalizing (Ferrara and Fishback, 2022;Fouka, 2019). The third option consists basically of keeping one's situation as it is.…”

Section: Thinking About Foreigners' Response To Violencementioning

confidence: 99%

“…Exposure to violence reduces intermarriage rates (Fouka, 2020;Gould and Klor, 2016), knowledge of the host country's language (Steinhardt (2018), though see Chen and Xie (2020)), support for the host country's political system (Fouka, 2020;Grewal and Hamid, 2022). In contrast, others scholars have shown that individuals exposed to violence tend to pick for their newborns names common in the host country (Chen and Xie, 2020;Fouka, 2019;Saavedra, 2021), adopt the cultural practice of their host community (Jaschke et al, 2022), and apply more for naturalization (Fouka, 2019).…”

Section: Thinking About Foreigners' Response To Violencementioning

confidence: 99%

In or Out? Xenophobic Violence and Foreigner Integration. Evidence from 19th century France

Emeriau,

Wolton

2024

Preprint

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How do foreigners respond to xenophobic violence? Do they move out, leaving their home, or do they buy in, assimilating further? We develop a stylized theoretical framework to explain why exposure to violence can yield both more exits and more assimilation. We use an exogenous spike in xenophobic violence in 19th-century France and fine-grained individual data to provide causal evidence of this dual effect. We also study how foreigners’ response varies with the degree of exposure to violence and their prior level of integration in the host country, highlighting the importance of thinking of foreigners as a heterogeneous group.

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Kenji or Kenneth? Pearl Harbor and Japanese-American assimilation

Cited by 16 publications

References 39 publications

Language-Agnostic Bias Detection in Language Models with Bias Probing

Language-Agnostic Bias Detection in Language Models with Bias Probing

Racial Discrimination and the Social Contract: Evidence from U.S. Army Enlistment during WWII

In or Out? Xenophobic Violence and Foreigner Integration. Evidence from 19th century France

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