Analyze

As of 13 February 2023, South Africa declared a national state of electricity disaster. In this paper we consider the impacts of global tech giants on the land, environment, people, heritage, and the technological landscape in Cape Town, South Africa. Our methods consist in long-term ethnographic fieldwork (Waltorp 2010, 2019, Waltorp et al 2022) and decolonial design anthropological approaches (Kambunga 2023) as we work with a group of local assistants and critical friends (www.digisatproject.com). We start from the controversy surrounding Amazon Web Services Headquarters: In 2021, the Observatory Civic Association and the Goringhaicona Khoi Khoi Indigenous Traditional Council filed an urgent notice with the High Court of South Africa to interject the construction of the Amazon River Park development on sacred land, where confrontations between the Peninsula Khoekhoe and the first Dutch settlers took place (genesis of colonialism in South Africa), and one of the only natural floodplains in Cape Town. Respondents argued that the site has no visible heritage significance, and the interjection will hinder economic development and job creation, an urgent concern, with Cape Town home to the most data centres on the continent. Data centres provide the computing and storage power that is essential to realising the smart digital futures furthered by corporate strategists and government policymakers. Yet, the data centres that underpin these futures are themselves energy-intensive enterprises (Howe et al. 2015) placing burdens on national energy supplier Eskom and energy shortages for the neighbouring communities (Pollio and Cirolia 2022). Data are entangled with water, wind, oil and other elements. Resource prospecting and extraction of energy were driving forces of colonial expansions. The material effects this has had on contemporary human and more-than-human life as well as geopolitical formations continue: How might we think together beyond techno-solutionism and -determinism to imagine technological futures otherwise.

Waltorp, Karen and Asnath Paula Kambunga. 2023. "Land, Legacies and Energy Futures in Cape Town, South Africa." In 4S Paraconference X EiJ: Building a Global Record, curated by Misria Shaik Ali, Kim Fortun, Phillip Baum and Prerna Srigyan. Annual Meeting of the Society of Social Studies of Science. Honolulu, Hawai'i, Nov 8-11.

you have read Tim Cresswell's book 地方：記憶、想像與認同

can you apply the theory to conceptualize the stories collected from Naluwan--what did the grandma's story teach us about Naluwan people's sense of place and belong when life forced them to move and live in between Meishan and Xianshan, among different ethnic culture?

Creators of the Student Health Index recommend using the tool in combination with qualitative data collection and stakeholder/community engagement (e.g. working with school leaders, local community leaders, and healthcare providers).

A full guide to using the dashboard is available here.

Data sources utilized by the index are not always the most current due to data collection limitations (e.g. covid-19 has caused disruptions in the collection of CDE data).

The Index is limited in that it does not offer data for schools that were not large enough to warrant the construction of a School-based Health Center. Thus, schools that did not meet specific enrollment targets were excluded from the dashboard. This includes rural schools (designed as such by the USDA) with an enrollment under 500 students, urban schools (without a high school) with less than 500 students, and urban schools (with a high school) with less than 1000 students. California had more than 10,000 active public schools in 2020-21. The final dashboard for the Student Health Index includes 4,821 schools.

The lack of available data on health indicators at a school-level restricted the Student Health Index to using proxies for the health outcomes. Some health indicators are included, but they are not school-specific, instead linked to specific schools geographically through the census tract. However, community-level data does not always accurately reflect the characteristics of a school’s population. As a result, school-level indicators in the Index were weighted more heavily than community-level indicators.

Additionally, race was not included as a measure in the Student Health Index because of California’s Proposition 20, which prohibits the allocation of public resources based on race and ethnicity. However, the dataset does contain measures of non-white students at each school. 

The Index has also been limited as a quantitative measure of need, which may overlook the influence of other factors that might be better illuminated through qualitative evidence (e.g. stakeholder engagement, focus groups, interviews, etc.).

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The Student Health Index can produce visualizations that represent data on conditions, school characteristics and risk factors that affect education outcomes and could be improved through access to school-based health care. These visualizations can be used to demonstrate need for expanding school-based health care access in California.

In addition to maps, the index can also be used to generate graphs and visual displays of data (e.g. ratio of highest need schools to all schools, by county).

The visualizations can be used to demonstrate the correlations between final need scores and race, the impact of specific indicators in health, and the concentration of need to certain regions of California (hot spot analysis).

The Student Health Index enables users to identify where SBHCs will have the most impact for students. The index uses 12 indicators, each of which can be scored from 1 to 4 for any given school. These scores are generated using percentiles and represent relative values. The 12 indicator scores are combined into a Need Score, which is calculated using percentiles along a scale of 1 to 4. Schools with a score of 4 (in the 4th quartile) have the highest Need scores relative to other schools in California.

The index is composed of 12 diverse indicators (percentages, rates, and index values) that have been transformed using percentiles in order to enable comparisons on a common scale. These indicators are divided into 3 categories: health indicators, school-level indicators, and socioeconomic indicators.

Health Indicators

1. Diabetes
2. Asthma ED admissions
3. Teen birth
4. Health Professional Shortage Areas (HPSA)

Socioeconomic Indicators

1. Poverty among individuals under 18
2. Uninsured among under 19
3. Healthy Places Index

School-Level Indicators

1. Percent FRPL (students eligible for free or reduced-price meals)
2. Percent English Learners
3. Percent Chronically Absent
4. Percent experiencing homelessness
5. Suspension rate

Other Data

1. Mental health hospitalization rate
2. Percent in foster care

Indicator selection was guided by CDC estimations on the primary contributing factors that shape health (social determinants of health, medical care, and health behaviors). The indicators included in the index are all either directly associated with the absence of health services that could be provided at a school level, act as proxies for health behaviors, or represent social determinants of health that could be addressed through access to school-based health services.

Indicator selection was influenced by recommendations from the Research Initiative of the Campaign for Educational Equity at Columbia Teachers College, which found that seven health disparities affecting school-aged youth could be addressed through school health programs. These disparities include: (1) vision, (2) asthma, (3) teen pregnancy, (4) aggression and violence (including bullying), (5) physical activity, (6) hunger, and (7) inattention and hyperactivity.

More detailed description of the rationale shaping indicator selection is available here.

Census tract, school-level

The Student Health Index draws from data that is publicly available and up to date on a statewide level. Sources include the University of California San Francisco Health Atlas, the American Community Survey, the U.S. Census Bureau, the California Department of Education’s Downloadable Data Files site, and the CDC.

Detailed list of sources:

PLACES Project, CDC (available through the UCSF Health Atlas)

CalEnviroScreen (available through the UCSF Health Atlas)

Opportunity Atlas (available through the UCSF Health Atlas)

Health Resources and Services Administration (available through the UCSF Health Atlas)

American Community Survey (available through the UCSF Health Atlas)

California Department of Education’s Downloadable Data Files site

Kidsdata.org