To estimate county of residence of Filipinx healthcare workers who died of COVID-19, we retrieved data from the Kanlungan website during the month of December 2020.22 In deciding who to include on the website, the AF3IRM team that established the Kanlungan website set two standards in data collection. First, the team found at least one source explicitly stating that the fallen healthcare worker was of Philippine ancestry; this was mostly media articles or obituaries sharing the life stories of the deceased. In a few cases, the confirmation came directly from the deceased healthcare worker's family member who submitted a tribute. Second, the team required a minimum of two sources to identify and announce fallen healthcare workers. We retrieved 86 US tributes from Kanlungan, but only 81 of them had information on county of residence. In total, 45 US counties with at least one reported tribute to a Filipinx healthcare worker who died of COVID-19 were identified for analysis and will hereafter be referred to as “Kanlungan counties.” Mortality data by county, race, and ethnicity came from the National Center for Health Statistics (NCHS).24 Updated weekly, this dataset is based on vital statistics data for use in conducting public health surveillance in near real time to provide provisional mortality estimates based on data received and processed by a specified cutoff date, before data are finalized and publicly released.25 We used the data released on December 30, 2020, which included provisional COVID-19 death counts from February 1, 2020 to December 26, 2020—during the height of the pandemic and prior to COVID-19 vaccines being available—for counties with at least 100 total COVID-19 deaths. During this time period, 501 counties (15.9% of the total 3,142 counties in all 50 states and Washington DC)26 met this criterion. Data on COVID-19 deaths were available for six major racial/ethnic groups: Non-Hispanic White, Non-Hispanic Black, Non-Hispanic Native Hawaiian or Other Pacific Islander, Non-Hispanic American Indian or Alaska Native, Non-Hispanic Asian (hereafter referred to as Asian American), and Hispanic. People with more than one race, and those with unknown race were included in the “Other” category. NCHS suppressed county-level data by race and ethnicity if death counts are less than 10. In total, 133 US counties reported COVID-19 mortality data for Asian Americans. These data were used to calculate the percentage of all COVID-19 decedents in the county who were Asian American. We used data from the 2018 American Community Survey (ACS) five-year estimates, downloaded from the Integrated Public Use Microdata Series (IPUMS) to create county-level population demographic variables.27 IPUMS is publicly available, and the database integrates samples using ACS data from 2000 to the present using a high degree of precision.27 We applied survey weights to calculate the following variables at the county-level: median age among Asian Americans, average income to poverty ratio among Asian Americans, the percentage of the county population that is Filipinx, and the percentage of healthcare workers in the county who are Filipinx. Healthcare workers encompassed all healthcare practitioners, technical occupations, and healthcare service occupations, including nurse practitioners, physicians, surgeons, dentists, physical therapists, home health aides, personal care aides, and other medical technicians and healthcare support workers. County-level data were available for 107 out of the 133 counties (80.5%) that had NCHS data on the distribution of COVID-19 deaths among Asian Americans, and 96 counties (72.2%) with Asian American healthcare workforce data. The ACS 2018 five-year estimates were also the source of county-level percentage of the Asian American population (alone or in combination) who are Filipinx.8 In addition, the ACS provided county-level population counts26 to calculate population density (people per 1,000 people per square mile), estimated by dividing the total population by the county area, then dividing by 1,000 people. The county area was calculated in ArcGIS 10.7.1 using the county boundary shapefile and projected to Albers equal area conic (for counties in the US contiguous states), Hawai’i Albers Equal Area Conic (for Hawai’i counties), and Alaska Albers Equal Area Conic (for Alaska counties).20

"Text Author Scholarship Metadata.tab" includes all the metadata on primary text titles, publication years, authorial gender, authorial race and ethnicity, and scholarship year that we collected and used to derive the results on contemporaneity, gender balance, and ethnic inequalities. Some of the metadata we used was proprietary to the MLA Bibliography so we cannot share more information on each piece of scholarship that cites an Asian American primary text. We have included accession numbers that will take you to the relevant record in the MLA bibliography (and DOIs for scholarship from Amerasia journal, which is not indexed in the MLA bibliography). "Chinese and Filipinx ethnic specific and panethnic citations.tab" includes all the metadata we used to calculate the results presented in figures 6 and 7. The citation counts under the panethnic label were derived from the metadata in "Text Author Scholarship Metadata.tab". The citation counts under the ethnic specific labels were collected from the MLA bibliography through searches for "Chinese American" "Filipino American/Filipino/Filipina" in the titles and abstracts of scholarly works. The topic modeling results in the article were based on a corpus accessed through the HathiTrust Research Center, with about 100 additional texts we digitized ourselves since they are not available in Hathi. "Topic modeling corpus composition.tab" shows the texts in that corpus and their HathiTrust IDs if the text was from Hathi. (Note that the corpus includes some cited pieces that are part of larger collections—a short story in a story collection, for instance. The Hathi IDs listed for such works are IDs for the whole collection. We cut down such texts to just the piece cited before topic modeling them. There are also instances where both a piece from a collection and the whole collection were cited. In those instances, we included both the whole collection and the piece in our corpus.) "Topics, top words, ethnic coding.tab" shows the topics generated from this corpus when we ran MALLET, the top 50 words in each topic, and how we coded each topic for ethnic affiliation. "Topic percentages in chunked texts.tab" shows the proportional makeup measure MALLET attributed to each topic for each 1000-word text chunk in the corpus. We averaged the proportional makeup percentages for ethnically affiliated topics across all the chunks of a text and then weighted these results by the number of times the text has been cited in Asian Americanist scholarship. Those results are presented in "Percentages of ethnically coded topics in whole texts weighted by citations.tab".