Matthew Connelly, professor, works in international and global history. He received his B.A. from Columbia (1990) and his Ph.D. from Yale ( 1997). His publications include A Diplomatic Revolution: Algeria's Fight for Independence and the Origins of the Post-Cold War Era (2002), and Fatal Misconception: The Struggle to Control World Population (2008). He has written research articles in Comparative Studies in Society and History, The International Journal of Middle East Studies, The American Historical Review, The Review francaise d'histoire d'Outre-mer, and Past & Present. He has also published commentary on international affairs in The Atlantic Monthly and The National Interest. more
Fatal Misconception is the first global history of a movement that sought to remake humanity- seemingly with the best of intentions-but succeeded in causing untold suffering. Beginning with eugenics, the temptation to breed better people culminated in the sterilization camps of India and the horrors of China’s one-child policy.Get it here
A Diplomatic Revolution describes how rebels can harness their cause to global trends to defeat an empire. It happened a half century ago, when Algerian nationalists mobilized Muslim immigrants in France and across Europe, staged urban terror to attract the international media, and finally won over the U.N. without ever liberating national territory.Get it here
Comment le FLN a-t-il fait, alors que ses troupes étaient écrasées par l’armée française, pour amener de Gaulle et le gouvernement de la France à accepter l’indépendance? La réponse se trouve bien au-delà des frontières de l’Algérie, car c’est sur la scène internationale que les nationalistes ont livré leurs combats les plus décisifs.Get it here
Documents as Data
Matthew Connelly is the principal investigator at History Lab. The mission of the history lab is to use data science to recover and repair the fabric of the past. We are beginning with declassified documents, which include some of the earliest examples of electronic records. By bringing together fragmented collections in a common database, we can use natural language processing and machine learning tools to explore them. The ultimate goal is to develop history as a data science so that citizens can keep government accountable in the age of big data.
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