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main argument, narrative and effect of this text

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Drawing on a long career as a Black critical health equity researcher, Bowleg quotes Black feminist Audre Lorde in arguing that the “master’s tools”—in order words, conventional theories and methods—"will never dismantle the master’s house”—intersectional structures of oppression from which health inequities are produced. Bowleg elaborates by explaining that conventional theories and methods “valorize almost exclusively individualistic and social cognitive approaches (Cochran & Mays, 1993; Weber & Parra-Medina, 2003); ignore the foundational roots of structural and intersectional inequality (Bowleg, 2012, 2020); center White, Western, cisgender male, middle-class, and heterosexual people and their experiences as normative (Henrich et al., 2010); prioritize amelioration, not transformation (Fox et al., 2009a); and view Black people primarily through the lens of deficit or pathology” (237).

 

Thus Bowleg offers 10 critical lessons for Black and other health equity researchers of color that she links with system and structural-level strategies. Bowleg also cautions that these lessons are risky and could damage one’s academic career—but that it is exactly this kind of risk that is necessary for change. Among these include: embrace critical perspectives, embrace a critical qualitative stance, learn research paradigms (e.g. positivist paradigm = a master’s tool, must learn to counter), foster community-based partnerships and collaborations, and highlight black communities’ strengths, assets, and acts of resistance. Bowledge also encourages researchers to “tell it like it is”: “Epistemological ignorance is one of the master’s most formidable tools. Epistemologies of ignorance refer to the examination of different types of ignorance and their production, maintenance, and functions (Sullivan & Tuana, 2007)” (239). Here, Bowleg emphasizes the importance of language by discussing how it can alternatively reveal or obscure structures of oppression as well as it shapes the nature of research.

 

9. How has this data resource been critiqued or acknowledged to be limited?

annlejan7
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There are missing data points within the dataset (attributed to non-reported information). This dataset has also been acknowledged to be limited in its prioritization of government data, which could have political limitations that may skew the degree of severity for disasters reported. 

8. What can be demonstrated or interpreted with this data set?

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This dataset can be used to demonstrate the geographic distribution of disasters in Vietnam over time. This database recognizes multiple dimensions of disaster, including natural (typhoons, hurricanes), technological (a chemical spill, a factory explosion), and more

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complex disasters such as famine.

6. How has this data resource been used in research and advocacy?

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This resource has been used in a publication written by Hoang et al., 2018 on the economic cost of the Formosa Toxic Waste Disaster in Central Vietnam. It is specifically used within the journal article to highlight the forms in which disasters can take place within a nation, and the rising cases of industrial disasters that have afflicted vulnerable communities within the last decade. This characterization sets the stage and context for the Formosa disaster, and integrates it within a wider conversation about the effects of intensified industrialization on the environment. 

5. What steps does a user need to take to produce analytically sharp or provocative data visualizations with this data resource?

annlejan7
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These datasets all involve  a strong spatial component. The presentation of such data could best be done via GIS Software, with their integration within a story map to demonstrate the importance of environmental stewardship to natural environments as well as the people who depend on such resources for their livelihoods.  For example, EPI data can be incorporated with EM-DAT’s disaster data to better understand the relationship between  a country’s EPI performance and the amount of technological disasters it observes. A country’s EPI score on Fish Stock Status can be compared with how much the nation’s GDP relies on fisheries to draw attention to discrepancies between stewardship and a country’s reliance on this resource. This process will require a user to be familiar with GIS Software and spatial plotting of data points (as the datasets themselves have not been integrated into ArcGIS), and using this software to integrate information together into meaningful maps.

4. What data visualizations illustrate how this data set can be leveraged to characterize environmental injustice?

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[Source: EM-DAT Public] This graphic shows the prevalence of technological disasters [includes toxic spills, industrial explosions, etc.] by country. This can be used to characterize, on a transnational level, where potential industrial harms are centralized or concentrated. While it does not characterize more insidious harms, such as air pollution, it can be a direct and easy to understand measure of environmental harm distribution across the globe. 

Additionally, data is available as excel sheets, which allows users to produce their own graphics on the prevalence of disasters within a particular nation over a desired time interval. 

3. Who makes this data available and what is their mission?

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This was developed in 1988 by personnel from the Center for Research on the Epidemiology of Disasters (CRED) within the Université catholique de Louvain (UCLouvain) with funding from the Belgian government and the World Health Organization (WHO), this data source aims to provide free open access information for users affiliated with academic organizations, non-profits, and international public organizations looking to gain understanding on the distribution  of disaster occurrences around the globe.

2. What data is drawn into the data resource and where does it come from?

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The EMT disaster database is compiled from a wide variety of sources, including UN agencies, NGOs, insurance companies, research institutes, and press agencies. The dataset compilation process prioritizes data from UN agencies, the International Federation of Red Cross and Red Crescent Societies, and government agencies. Entries are reviewed prior to consolidation, and this process of checking and incorporating data is done on a daily basis. More routined  data checking and management also occurs at a monthly interval, with revisions made at the end of each year.