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1. Anirvan Chatterjee (anirvan@mastodon.social)'s status on Monday, 27-Feb-2023 07:35:08 JST Anirvan Chatterjee

   Happy #BlackHistoryMonth!

   I want to share something really cool that I discovered.

   I've been digging into intersections between Black and #SouthAsianAmerican history, and found something new.

   We know about Punjabi Mexicans and Black Bengalis.

   But I just found 3 examples of Black women in New York and Chicago married to Punjabi/Sikh men back in 1940.

   👇🏽 Keep reading for the whole story

   1/n

   #USHistory #AsianAmerican #EthnicStudies #BlackHistory #Geneadons #Histodons #Sikh #MastIndia #Desi

   • Peter Pappas (edteck@mastodon.coffee)'s status on Monday, 27-Feb-2023 07:35:05 JST Peter Pappas

     in reply to

     • Simon Willison

     Great work. Here’s how my students used the 1940 Census to make a personal connection with historical redlining maps : "Mapping Inequality: Exploring Personal History in Redline Maps and the 1940 Census" https://peterpappas.com/2020/11/mapping-inequality-exploring-personal-history-in-redline-maps-and-the-1940-census.html #BlackHistoryMonth #TeachTruth #Histodon #Histodons #Redlining #History @anirvan @simon

   • Anirvan Chatterjee (anirvan@mastodon.social)'s status on Monday, 27-Feb-2023 07:35:07 JST Anirvan Chatterjee

     in reply to

     The first significant wave of South Asians on the West Coast of the US were Punjabi men, primarily Sikh (https://en.wikipedia.org/wiki/Punjabi_Americans)

     Some of those histories are well recorded: Stockton Gurdwara, railroads, Punjabi-Mexican marriages, Bellingham Riot: https://en.wikipedia.org/wiki/Punjabi_Americans

     I wanted to do some #DigitalHumanities analysis, so I created a cleaned-up list of every person with the last name "Singh" in the 1940 US Census.

     I made the dataset public:

     https://github.com/anirvan/1940-singh-census/
     https://en.wikipedia.org/wiki/Singh

     2/n

   • Anirvan Chatterjee (anirvan@mastodon.social)'s status on Monday, 27-Feb-2023 07:35:07 JST Anirvan Chatterjee

     in reply to

     What do you with a history dataset?

     I started by exploring the data with #Tableau.

     Here's what I discovered about the Singhs in the 1940 US Census:

     • most were in California

     • immigrant men had an average age 51 (so old!) — maybe due to 1920s immigration restrictions?

     • the majority of non-Indian wives were from former/current Mexico

     • Singh men could be designated "Hindoo" or "White"

     I wrote this blog post that explains why:

     https://secretdesihistory.com/the-1940-singh-census-and-the-attack-on-indian-immigrants-7fcd8e16c5e

     3/n

   • Anirvan Chatterjee (anirvan@mastodon.social)'s status on Monday, 27-Feb-2023 07:35:07 JST Anirvan Chatterjee

     in reply to

     • Simon Willison

     I've been testing @simon's amazing #Datasette tool, which makes it easy to explore CSV and JSON datasets: https://datasette.io

     Simon has a version that runs in your (desktop) web browser, so I pointed it at my dataset.

     Here's the link if you want to see for yourself:

     https://lite.datasette.io/?json=https%3A%2F%2Fraw.githubusercontent.com%2Fanirvan%2F1940-singh-census%2Fmaster%2F1940_singh_census_basic.json#/data/1940_singh_census_basic?_filter_column_1=&_filter_op_1=exact&_filter_value_1=%5B%22non-South+Asian+wife%22%5D&_filter_column_2=&_filter_op_2=exact&_filter_value_2=Black&_filter_column=&_filter_op=exact&_filter_value=&_sort=rowid&_facet=gender&_facet=inferredGroups&_facet=raceRecordedNormalized&_facet=censusLocationNormalized&_facet=birthPlaceNormalized

     I spent a few minutes exploring the facets, when I found three Black women with the last name of Singh in the 1940 U.S. Census (!!!)

     This is the exact query I ran:

     https://lite.datasette.io/?json=https%3A%2F%2Fraw.githubusercontent.com%2Fanirvan%2F1940-singh-census%2Fmaster%2F1940_singh_census_basic.json#/data/1940_singh_census_basic?_sort=rowid&_facet=gender&_facet=inferredGroups&_facet=raceRecordedNormalized&_facet=censusLocationNormalized&_facet=birthPlaceNormalized&raceRecordedNormalized=Black&inferredGroups=[%22non-South+Asian+wife%22

     4/n

   • Christi Junior (christijunior@bae.st)'s status on Monday, 27-Feb-2023 07:35:27 JST Christi Junior

     in reply to

     • Simon Willison
     • Peter Pappas
     @edteck @anirvan @simon @edteck @anirvan @simon Niggers are inherently inferior, as in being dumber, lacking in impulse control and being naturally more violent, that's the real reason behind muh inequality.
   • Charliebrownau (charliebrownau@pieville.net)'s status on Monday, 27-Feb-2023 10:06:15 JST Charliebrownau

     in reply to

     • Christi Junior
     • Simon Willison
     • 🆒Ｅｌｉ👑
     • Peter Pappas

     @zoomgoboom @ChristiJunior @simon @edteck @anirvan

     Anyone else noticing the self hating Whites, follow ((( whites))) , liberals and commies will always goto white suburbs also

     While pushing for non White Multi racialism

     Drew from Sourcehut classic example
     Moves to a majority white EU country, fleas from USA while still supporting International (((( commie))) , ((( multi culturalism ))) and ((( ANTI RACIST)))

   • Christi Junior (christijunior@detroitriotcity.com)'s status on Monday, 27-Feb-2023 10:06:16 JST Christi Junior

     in reply to

     • Simon Willison
     • Peter Pappas
     @edteck @anirvan @simon >Mapping Inequality: Exploring Personal History in Redline Maps and the 1940 Census
     
     Sounds fascinating. You know what's also fascinating? The way FBI crime stats count Latinos, Arabs and various other sand people as "White", simply to make White people to seem way more crime-prone than they really are - and despite this statistical trickery, black people are STILL shown to commit violent crime at something like 8 times the rate of "White" people.
   • 🆒Ｅｌｉ👑 (zoomgoboom@detroitriotcity.com)'s status on Monday, 27-Feb-2023 10:06:16 JST 🆒Ｅｌｉ👑

     in reply to

     • Christi Junior
     • Simon Willison
     • Peter Pappas
     @ChristiJunior the poorest white neighbourhood has less crime than the wealthiest black neighbourhood, areas that have the highest degree of cosanguinuity (marrying too closely) are in the same regions that have lowest IQ scores. Hmmm, now I wonder what the predominant skin colour is? (accounting for mixed-race people)
     
     Yep, generally quite dark/black @edteck @anirvan @simon