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{ "item_title" : "Semantics in Political Science Research", "item_author" : [" Pedro L. Rodr´ýguez Sosa "], "item_description" : "This is a three-part project concerning methods for the study of political semantics -how the 'meaning' of political concepts is represented and organized in memory and implications for political attitudes and behavior. The first chapter proposes a framework for the estimation of group differences in memory representations of political concepts and applies it to evaluate partisan representational differences in the U.S. The second chapter proposes amemory-centered approach to the study of ideology along with the requisite methods for its implementation. The third chapter centers on word embeddings, a deep learning method to estimate word representations from large collections of text. Along with a conceptual overview, it provides practitioners with a series of tests to perform model comparison and validation, including a novel Turing-style test", "item_img_path" : "https://covers3.booksamillion.com/covers/bam/8/45/492/350/8454923500_b.jpg", "price_data" : { "retail_price" : "28.00", "online_price" : "28.00", "our_price" : "28.00", "club_price" : "28.00", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Semantics in Political Science Research|Pedro L. Rodr´ýguez Sosa

Semantics in Political Science Research

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Overview

This is a three-part project concerning methods for the study of political semantics -how the 'meaning' of political concepts is represented and organized in memory and implications for political attitudes and behavior. The first chapter proposes a framework for the estimation of group differences in memory representations of political concepts and applies it to evaluate partisan representational differences in the U.S. The second chapter proposes a

memory-centered approach to the study of ideology along with the requisite methods for its implementation. The third chapter centers on word embeddings, a deep learning method to estimate word representations from large collections of text. Along with a conceptual overview, it provides practitioners with a series of tests to perform model comparison and validation, including a novel Turing-style test

Details

  • ISBN-13: 9788454923502
  • ISBN-10: 8454923500
  • Publisher: Shetty Publishers
  • Publish Date: December 2023
  • Dimensions: 9 x 6 x 0.21 inches
  • Shipping Weight: 0.32 pounds
  • Page Count: 102

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