Page de couverture de Raiders Of The Lost Archive

Raiders Of The Lost Archive

Auteur(s): Jesse Driscoll & Christian Davenport
  • Résumé

  • TRUE TALES of Discovery in the Social Sciences! Brought to you by Christian Davenport & Jesse Driscoll.
    Copyright 2021 All rights reserved.
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Épisodes
  • Episode 22 - Wendy Pearlman
    Oct 16 2023

    Wendy Pearlman is Professor of Political Science at Northwestern University, where she is Crown Professor of Middle East Studies. She studies the comparative politics of the Middle East, social movements, and forced migration, and has conducted with more than 500 displaced Syrians since 2012.  In this podcast we discuss how this data was curated ("midwife-ing") to create the award-winning We Crossed A Bridge and It Trembled: Voices from Syria (HarperCollins, 2017).  

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    59 min
  • Episode 21 - Kristine Eck
    Jun 12 2023

    Kristine Eck (Uppsala University) discusses the challenges of working with contemporary and historical police archives.  For quantitative social scientists, how does "the data generation process" introduce measurement bias into the processes that we are actually describing when we employ data generated by the state for counterinsurgency?  How do ongoing state efforts to digitize archives aid and hinder political scientists and data scientists trying to quantify human rights abuses?  What are the citation norms for private correspondence by public figures who were in command decision roles during episodes of violence?  A frank, eye-opening discussion on how scholars access and use state-generated datasets on repression, with comparative cases ranging from the Malayan Emergency to Israel and from contemporary OECD countries to Nepal. 

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    52 min
  • Episode 20 - Ana Bracic
    May 19 2023

    Ana Bracic (Michigan State University) discusses positionality, entree, and a variety of ethical considerations that informed her work with a highly-vulnerable population in Central Europe.  The candid discussion of how her project on the Roma evolved from an idealized, perfect "magical dataset" ("something as ridiculous as a time-series cross-section dataset on some sort of dimension of Roma exclusion, and it didn't matter what it was, so long as it was the same one across all these observations...") to her actual project.  How was her mother an asset?  What happens if you come home from the field with data that, once analyzed, is shown to be completely unhelpful for your job market paper?  A can't miss episode for an aspiring junior scholar.

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    55 min

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