Italian Marxist philosopher, writer, and politician (1891–1937)
Antonio Gramsci was an Italian Marxist philosopher and politician whose ideas about how powerful groups maintain control through culture and shared beliefs—not just force—have deeply influenced political thought and cultural analysis across the world. His work matters because it offers a way to understand how societies function beyond just economics, making his theories relevant to scholars studying politics, culture, and social change today.
AI-generated from the Wikipedia summary — may contain errors.
via Open Library + Wikidata
Writing
via TMDB
<a href="https://www.last.fm/music/Antonio+Gramsci">Read more on Last.fm</a>
Antonio Francesco Gramsci ( UK: /ˈɡræmʃi/ GRAM-shee, US: /ˈɡrɑːmʃi/ GRAHM-shee; Italian: [anˈtɔːnjo franˈtʃesko ˈɡramʃi] ; 22 January 1891 – 27 April 1937) was an Italian Marxist philosopher, journalist and politician. He was a founding member and one-time leader of the Italian Communist Party. A vocal critic of Benito Mussolini and fascism, he was imprisoned in 1926, and remained in prison until shortly before his death in 1937.
During his imprisonment, Gramsci wrote more than 30 notebooks and 3,000 pages of history and analysis. His Prison Notebooks are considered a highly original contribution to 20th-century political theory. Gramsci drew insights from varying sources—not only other Marxists but also thinkers such as Niccolò Machiavelli, Vilfredo Pareto, Charles Darwin, Sigmund Freud, Friedrich Nietzsche, Georg Hegel, Pierre Joseph Proudhon, Georges Sorel, and Benedetto Croce. The notebooks cover a wide range of topics, including the history of Italy and Italian nationalism, the French Revolution, fascism, Taylorism and Fordism, civil society, the state, historical materialism, folklore, religion, and high and popular culture.
5 total works indexed
· 2010 · cited 30,698x
· 2019 · cited 19,828x
· 2020 · cited 15,235x
· 2022 · cited 12,959x
· 2010 · cited 11,279x
via Crossref · CC0
via Wikidata · CC0
via Wikidata · CC0
Discovered by embedding cosine similarity (sentence-transformers MiniLM, 384-dim).