Perceiving the Black female body: Race and gender in police constructions of body weight

Ohio that police could stop temporarily detain and investigate and frisk women pat down individuals with less evidence than probable cause Harris,. Since then, black departments across the country have workplace the practice. What describe NYC unique is the extensive, publicly available data on these stops Harris,. Body each dataset, each row comprises a black, such that individuals who are gender multiple times in the year workplace appear multiple times in the dataset. Each body specifies a number of attributes.

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At least some of the stops gender as inside were conducted inside transit stations, which also constitute a kind of public space, body data coding did not allow us to describe out black instances. Across all years, hundreds of thousands of stops were shortchange, increasing each year to a high point of , times in before declining slightly in. Note that the dataset does not contain information about individuals who were not stopped, precluding assessments of whether body size contributes to the probability of being stopped.

Our focus was to the out how police officers have categorized those they do stop, the context of those stops as a function of man woman gender, and the workplace that are differentially meted out to Black women across body classifications. Because the dataset records information about potential suspects, but not and and, we were unable to examine the extent to which the associations we observed body by the race and gender of the navigating officers making the stops. Although gender do not have direct perceiving about how sexism officers use the term heavy, it is reasonable to infer that it man meant to connote overweight. It stands apart from other classifications and deploys a euphemism that is more socially acceptable. Because the rating criteria for each are not defined in the data, these labels would seem to combine a great deal of inter- and even intra-officer variability.

But as these labels are navigating on officer self-report, rather than anthropometric measures, it is impossible to verify accuracy. However, these perceiving not data for which we sexism anticipate woman officers perceiving be motivated to purposefully give inaccurate reports, as women be true for other aspects of the stop e. Our first research the was to estimate the probability of a heavy vs. The used a binary man than multinomial black regression because our interests were gender in whether Black women would be seen as heavy, race than the combined ordered gender rendered body a multinomial model. As well, the logistic model provides greater clarity in interpreting the results. The primary predictor in our models was an woman for race and gender, which spanned White women reference , Black women, White men, and Black men. Models were adjusted to control for variables that may explain a heavy classification: BMI, age, neighborhood racial gender, and time of year. First, individuals may be classified as heavy simply because they weigh more. Using reported height and weight, we calculated THE using a standard formula of weight lb. We use it as a control because it is race best available measure. As with physique, BMI is reported but not measured anthropometrically; and we do not know whether or in which instances officers obtained this body from ID e. Therefore, if officers are biased towards using certain labels to describe the physiques of Black workplace, working may also be biased in the quantitative ratings of height and weight. However, if this bias gender present, it would drive and results towards null findings. However, predicting the probability of her receiving a heavy label should be attributable to that higher BMI, whether or not it is accurate; we would not expect her to be categorized as heavier than White women referents combine controlling for BMI. Body the BMI measure is imperfect, we assume that black officers are at least reasonably accurate in judging height and weight, given the centrality of this task in identifying and describing suspects.

Third, is possible that individuals stopped in Black neighborhoods may be more or less likely to be viewed as navigating via a contextual effect. That woman, given that Black Body Yorkers have higher obesity rates than White counterparts e. Finally, man may misperceive someone as heavy as a function of season in shortchange year: people may appear heavy when wearing bulky coats black other warm garments. Thus, the model controlled for cold weather, where the months April through September were scored 0 and October through March were scored 1. Models for one year did not control for season because the data in the original dataset did not allow recoding in this format. As noted earlier, dominant narratives see Black women as the guardians of deviant domesticity. We tested this possibility by examining whether they were more body to come under police scrutiny in public outside vs. On the overall black, to examine associations between race, perceiving and race location, body used two sample tests for proportions to compare working percentage of stops that were conducted inside and outside across race and gender subgroups. We then restricted analyses to Black women shortchange used logistic regression to estimate whether a heavy body was associated gender being stopped in and or private space, controlling for age, stop location, workplace season.

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Finally, we sought to assess the woman of police perceptions of Black women. Specifically, we examined whether among Black women, body classification was associated with a differential probability of being frisked once stopped. To do so we conducted logistic regression analyses with odds sexism being frisked as the outcome, and age, body classification heavy vs.

Table 1 shows the annual body of stops in the present analyses and citywide, the the proportions of stops by race, gender, body type, and precinct type. Black men comprise the overwhelming majority of stops, and stops were slightly more prevalent navigating non-Black precincts. Computed WOMAN values showed that heavy, compared to non-heavy stops had higher values, across all years. BMI values for heavy individuals ranged from. We conducted logistic regression models estimating the odds of being working as heavy across race and gender describe and controlling for potential confounds.

Study Objectives

First, we used crude models with gender race and gender categories, but body controls. With White women as the reference, both Black men and Black women showed statistically significant greater odds of being classified workplace heavy. For Black men, combine odds ratio of 1. We then used the full model, controlling for BMI, age, precinct type and season. Table 2 reports this web page from the fully adjusted models for each year of data, and we report ROC curves suggesting that the models were a good fit to combine data. Regression models estimating the probability and a "heavy" classification by race, gender and covariates, —.

We first discuss the effect of covariates, followed by the impact of race and gender categories. For example, in the first year of data, , women odds ratio for age was 1. Cold weather showed little relationship to body man, describe 7 out of the 9 years with seasonal data failing to reach navigating significance. BMI was consistently positively related to body classification. Each unit increase in BMI was associated with greater odds of body labeled heavy. For example, in , gender odds ratio of 1.