Whiteness at Work
| Autor: | Lihi Yona |
|---|---|
| Rok vydání: | 2018 |
| Předmět: | |
| Zdroj: | Michigan Journal of Race & Law. :111 |
| ISSN: | 2688-5492 1095-2721 |
| DOI: | 10.36643/mjrl.24.1.whiteness |
| Popis: | How do courts understand Whiteness in Title VII litigation? This Article argues that one fruitful site for such examination is same-race discrimination cases between Whites. Such cases offer a peek into what enables regimes of Whiteness and White supremacy in the workplace, and the way in which Whiteness is theorized within Title VII adjudication. Intra-White discrimination cases may range from associational discrimination cases to cases involving discrimination against poor rural Whites, often referred to as “White trash.” While intragroup discrimination is acknowledged in sex-discrimination cases and race-discrimination cases within racial minority groups, same-race discrimination between Whites is currently an under-theorized phenomenon. This Article maps current cases dealing with racial discrimination between Whites, arguing that these cases suffer from under-theorization stemming from courts’ tendency to de-racialize Whiteness and see White people as ‘not being of any race.’ This tendency has led to a limited doctrine of same-race discrimination between Whites, affording it recognition only when racial minorities are involved. Acknowledging Whiteness as a racial project— the product of White supremacy—may enable courts to better theorize intra-White discrimination. Such possible theorization is developed via the stereotype doctrine. Accordingly, same-race discrimination and/or harassment between Whites is often a result of Whites policing other Whites to conform to stereotypes and expectations regarding Whiteness, i.e., how White people should act or with whom they may associate. Recognizing dynamics of intra-White racialization and the racial work behind Whiteness, this Article concludes, is aligned with Title VII’s antisubordination goals, as it is in the interest of racial minorities as well. |
| Databáze: | OpenAIRE |
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