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Computational Stratification: Data-Intensive Social Science for Inequality & Policy photo

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Name: Computational Stratification: Data-Intensive Social Science for Inequality & Policy

Type: Organization

Bio: Computational social science of stratification & policy--esp. education, organizations, politics--and human costs, using web-scraping, text analysis, & modeling

Twitter: jhaber43

Location: UC Berkeley

Blog: https://www.jarenhaber.com

Computational Stratification: Data-Intensive Social Science for Inequality & Policy's Projects

data_management icon data_management

Code for managing large data sets in Python, usually with Pandas. These scripts mostly merge, filter, inspect, and count things. Developed for a charter school database of 10K+ units based on web-crawling and federal data sources (CCD, ACS, etc).

edunomics_arrays icon edunomics_arrays

Arrays of school level spending across student poverty/disadvantage for Edunomics Lab. DC and maybe other states/districts.

geospatial icon geospatial

Code that examines geographic patterns in charter school proliferation, size, performance, and especially ideology within race- and class-structured school districts and Census tracts. Key packages include matplotlib, folium, and geoplotlib.

scrape_obituaries icon scrape_obituaries

Code for scraping obituaries from Legacy.com. 3 steps: scrape URLs & paragraphs then extract age, sex, & race

scrapy-cluster icon scrapy-cluster

This Scrapy project uses Redis and Kafka to create a distributed on demand scraping cluster.

sorting-schools-2020 icon sorting-schools-2020

Replication code for "Sorting Schools: A Computational Analysis of Charter School Identities and Stratification" research article by Jaren Haber, UC Berkeley. Paper investigates the relationships between charter school and school district poverty & race, on one hand, and school ideology and academic performance, on the other.

text_analysis icon text_analysis

Code and data for research team that does text analysis: word counts, word embeddings, topic models, parsing HTML, unsupervised clustering, etc.

web-crawling-ic2s2-2022 icon web-crawling-ic2s2-2022

An introduction to web-crawling/scraping for beginners with some Python know-how. Created for IC2S2 Summer 2022 by Jaren Haber, PhD

web_scraping icon web_scraping

Code and data for the research team scraping charter websites using scrapy, requests, Selenium, and wget with Python, shell, and Docker. This is the foundation of analyses into charter schools' linguistic strategies and social implications.

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