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This article explores the contradictory history of lower castes converting out of Hinduism yet not out of lowliness or casteism in early twentieth-century Panjab. It begins by contextualizing what B. R. Ambedkar's undelivered 1936 Lahore speech on annihilating caste might have meant to an audience of largely landless agricultural laborers. Next it examines the changing constellation of caste names and occupational designations for these groups amid the emergence of the Ad Dharm movement and its struggle to impart equality, dignity, and community to Panjabi Dalits. To situate this new sense of identity in the context of actual labor practices, the article then analyzes the fraught relationship between landholding cultivators and landless laborers working side-by-side while continuing to be separate and unequal. That a large proportion of lower castes could adopt religions other than Hinduism or even start their own and yet remain excluded and exploited reveals the limits of a politics centered on conversion, as well as a different horizon for emancipation.

Current census debates in Brazil surrounding Brazilian race categories center on two contrasting proposals: the adoption of the multiracial moreno term vs. the use of the collective black classification negro. Those proposing the former base their argument on the right to self-classify according to one's own sense of identity. Proponents of the negro category contend that it would be most efficient for redressing racial discrimination. We examine the meaning and saliency of these categories and explore the possible consequences of their adoption. Using national survey data, we demonstrate how education, age, color, sex and local racial composition structure the choices of moreno and negro over official census terms. Findings include a negative correlation between education and the choice of moreno, while the opposite is true for negro. In addition, an age effect on both categories suggests a popular shift in racial labeling away from official census terms. We note that similar issues structure current census debates in the USA.

At the dawn of the twenty-first century, America finds itself on the brink of a new racial consciousness. The old, unquestioned confidence with which individuals can be classified (as embodied, for instance, in previous U.S. census categories) has been eroded. In its place are shifting paradigms and new norms for racial identity. Eva Marie Garroutte examines the changing processes of racial identification and their implications by looking specifically at the case of American Indians.

Current census debates in Brazil surrounding Brazilian race categories center on two contrasting proposals: the adoption of the multiracial term vs. the use of the collective black classification . Those proposing the former base their argument on the right to self-classify according to one's own sense of identity. Proponents of the category contend that it would be most efficient for redressing racial discrimination. We examine the meaning and saliency of these categories and explore the possible consequences of their adoption. Using national survey data, we demonstrate how education, age, color, sex and local racial composition structure the choices of and over official census terms. Findings include a negative correlation between education and the choice of , while the opposite is true for . In addition, an age effect on both categories suggests a popular shift in racial labeling away from official census terms. We note that similar issues structure current census debates in the USA.

Addresses the needs of preschool children whose biological parents come from two or more traditional racial/ethnic groups. Advocates the extension of multiracial curriculum in early childhood programs to support and embrace these multiracial and multiethnic children. Recommends staff training, providing new classroom materials, working closely with parents, and challenging the single-race approach to multicultural education. (JPB)

People of Middle Eastern and North African (MENA) descent are categorized as non-White in many Western countries but counted as White on the US Census. Yet, it is not clear that MENA people see themselves or are seen by others as White. We examine both sides of this ethnoracial boundary in two experiments. First, we examined how non-MENA White and MENA individuals perceive the racial status of MENA traits (external categorization), and then, how MENA individuals identify themselves (self-identification). We found non-MENA Whites and MENAs consider MENA-related traits—including ancestry, names, and religion—to be MENA rather than White. Furthermore, when given the option, most MENA individuals self-identify as MENA or as MENA and White, particularly second-generation individuals and those who identify as Muslim. In addition, MENAs who perceive more anti-MENA discrimination are more likely to embrace a MENA identity, which suggests that perceived racial hostility may be activating a stronger group identity. Our findings provide evidence about the suitability of adding a separate MENA label to the race/ethnicity identification question in the US Census, and suggest MENAs’ official designation as White may not correspond to their lived experiences nor to others’ perceptions. As long as MENA Americans remain aggregated with Whites, potential inequalities they face will remain hidden.

Functioning is the third health indicator besides mortality and morbidity. Although Korea periodically collects functioning information, the functioning indicator has not been generated yet. This study aimed to evaluate how functioning information is currently collected in Korea's health and disability surveys, especially in terms of comparability and comprehensiveness, using the International Classification of Functioning, Disability and Health (ICF) as a reference framework. Data sources included three health and disability surveys in Korea, namely the Population and Housing Census, the Korean National Health and Nutrition Examination Survey, and the Survey on the Living condition of Persons with Disabilities, and two international ICF-based surveys, the International Spinal Cord Injury Survey and the Model Disability Survey. Functioning information was linked by category to the ICF Generic-30 Set utilizing the ICF linking rules. Three ICF categories - d450 walking; d510 washing oneself; d540 dressing ̶ were covered by all the data sources. Excluding the Population and Housing Census, which inherently differs from the other data sources, all the surveys addressed six ICF categories - b152 emotional functions; b280 sensation of pain; d240 handling stress and other psychological demands; d450 walking; d510 washing oneself; d540 dressing. The ICF categories b152 emotional functions and b130 energy and drive functions were the most frequently covered across all the surveys. Despite of the common ICF categories, important differences in the operationalization of questions and response options were found. The overlap of content of health and disability surveys in Korea enables interoperability with other data sources. Available functioning data can be used for estimating the functioning indicator and metric, as recommended by WHO, which can serve as a valuable epidemiological indicator for complementing mortality and morbidity indicators and for estimating and monitoring rehabilitation or long-term care needs of the Korean population.

Collection of race and ethnicity data has been controversial in the United States, and the identification of legally recognized race and ethnic categories has shifted over time as a result of changes in the sociopolitical climate. For example, the shift from using the Asian (Mongol) to the White classification for Arab or Middle Eastern and North African (MENA) populations came after contentious court battles at the beginning of the 20th century, when being White was a prerequisite for US citizenship. Furthermore, the accuracy of racial and ethnic categories has been questioned.2-4 A case in point is that the Hispanic/Latino group did not emerge as a recognized ethnic group until the 1980 US census count,5 and there was little consensus as to whether this category should be considered a race or an ethnicity.6 The categories adopted in the US census tend to result from both research and political lobbying. The political motivations underlying the collection of race and ethnicity data range from remedying inequalities to advancing White supremacist values.4,7,8 Accurate and robust collection of ethnicity and race data is the first critical step in identifying and addressing disparities in health. One ethnic minority group that is often omitted from medical and health-related data collection altogether is the Arab/ MENA population in the United States. Race and ethnicity disparity statistics often exclude the Arab/MENA population because either data are not being collected on this population or the group is not being disaggregated from the White race category. A growing body of research shows that Arab/MENA Americans have both health and social patterns distinct from those of Whites. Findings suggest that, relative to nonHispanic Whites, Arab/MENA Americans have a higher prevalence of metabolic disorders and cardiovascular disease,11-13 as well as low birth weight14,15 and depressive symptoms.16,17 Furthermore, this group is bifurcated in terms of socioeconomic status, reporting both higher and lower educational levels than Whites.

As housing costs continue to increase across many cities in North America and Europe, local governments face pressure to understand how housing’s rising cost is changing neighbourhoods and to ensure that everyone can access a home they can afford. To confront displacement concerns, cities are adapting models developed within academia to identify neighbourhoods that may be susceptible to gentrification and displacement. We compare four gentrification and displacement risk models developed by and for the US cities of Seattle, Washington; Los Angeles, California; Portland, Oregon; and Philadelphia, Pennsylvania, and apply all four methodologies to one city, Boston. We identify the geographic areas of agreement and disagreement among the methods. The comparison reveals striking differences between the models, both in inputs and outputs. Of the 18 variables considered among the four models, only two variables appear in all four models. In the resulting maps, the four methods identified between 25 and 119 of the 180 Boston census tracts as at risk of gentrification and displacement, or as currently gentrifying. There are only seven tracts that all four models agreed were either gentrifying or at risk of gentrification and displacement. The findings indicate a need for cities to consider critically the assumptions of the models that are included in urban policy documents, as indicators and thresholds have major impacts on how neighbourhoods in the liminal space of gentrification and displacement are characterised. This novel comparison of United States local government analyses of gentrification provides insight as modelling moves from theory to practice. 随着北美和欧洲许多城市的住房成本持续上涨，地方政府面临着一种压力，即必须理解住房成本上涨给街区带来的改变，以及如何确保每个人都能住到自己能负担得起的房子。为了应对驱逐问题，各个城市正在改编学术界开发的模型，以确定可能容易受到绅士化和驱逐影响的街区。我们比较了美国华盛顿州西雅图、加利福尼亚州洛杉矶、俄勒冈州波特兰、和宾夕法尼亚州费城分别为自身开发的四个绅士化和驱逐风险模型，并将所有四种方法应用于一个城市：波士顿。我们识别在各种方法之间表现出一致和不一致的地理区域。这一比较揭示了模型之间在输入和输出方面的显著差异。在四个模型考虑的18个变量中，只有两个变量出现在所有四个模型中。在波士顿的180个人口普查区中，这四种法方生成的地图所确定的、有绅士化和流离失所风险（或者当前正在绅士化）的人口普查区的数量分别为25个到119个不等。只有七个人口普查区同时被所有四种模型确定为正处于绅士化过程中、或存在绅士化和驱逐风险。研究结果表明，城市需要批判性地考虑城市政策文件所使用的模型中所包含的假设，因为指标和阈值对于绅士化和流离失所阈限空间内的街区特征描绘有着重大影响。随着模型从理论走向实践，这种对美国地方政府绅士化分析的新颖比较提供了深刻的见解。

The first observationally based near-global shallow cumuliform snowfall census is undertaken using multiyear CloudSat Cloud Profiling Radar observations. CloudSat snowfall observations and snowfall rate estimates from the CloudSat 2C-Snow Water Content and Snowfall Rate (2C-SNOW-PROFILE) product are partitioned between shallow cumuliform and nimbostratus cloud structures by utilizing coincident cloud category classifications from the CloudSat 2B-Cloud Scenario Classification (2B-CLDCLASS) product. Shallow cumuliform (nimbostratus) snowfall events comprise about 36% (59%) of snowfall events in the CloudSat snowfall dataset. The remaining 5% of snowfall events are distributed between other categories. Distinct oceanic versus continental trends exist between the two major snowfall categories, as shallow cumuliform snow-producing clouds occur predominantly over the oceans. Regional differences are also noted in the partitioned dataset, with over-ocean regions near Greenland, the far North Atlantic Ocean, the Barents Sea, the western Pacific Ocean, the southern Bering Sea, and the Southern Hemispheric pan-oceanic region containing distinct shallow snowfall occurrence maxima exceeding 60%. Certain Northern Hemispheric continental regions also experience frequent shallow cumuliform snowfall events (e.g., inland Russia), as well as some mountainous regions. CloudSat-generated snowfall rates are also partitioned between the two major snowfall categories to illustrate the importance of shallow snow-producing cloud structures to the average annual snowfall. While shallow cumuliform snowfall produces over 50% of the annual estimated surface snowfall flux regionally, about 18% (82%) of global snowfall is attributed to shallow (nimbostratus) snowfall. This foundational spaceborne snowfall study will be utilized for follow-on evaluative studies with independent model, reanalysis, and ground-based observational datasets to characterize respective dataset biases and to better quantify CloudSat snowfall detection and quantitative snowfall estimate uncertainties.