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Triarchic Theory of Intelligence01:24

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知識の統合を測定するための新しいスケール

Cornelia Fischer1, Katharina Gugerell2,3, Ursula Laa4

  • 1Department of Economics and Social Sciences, BOKU University, Vienna, Austria.

Humanities & social sciences communications
|August 29, 2025
PubMed
まとめ
この要約は機械生成です。

新しいスケールは,学際的な研究が知識を統合する方法を測定し,社会情緒的,認知コミュニケーションの要因を明らかにします. このツールは,複雑な社会課題に取り組むための方法を比較するのに役立ちます.

キーワード:
環境調査科学,技術,社会

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科学分野:

  • 社会科学
  • 知識管理
  • 分野間の研究

背景:

  • 分野を超えた研究は,社会的な課題に対する多様な知識を統合します.
  • 測定ツールが不足しているため,学際的な方法の有効性は十分に研究されていません.
  • 知識の統合は学際的な成功の鍵ですが,その測定は複雑です.

研究 の 目的:

  • 知識統合における学際的メソッドの貢献を評価するための新しいスケールを開発し,実証的にテストする.
  • 分野を超えたアプローチの有効性を評価するための標準化されたツールを提供すること.
  • 異なる学際的な方法の比較分析を容易にする.

主な方法:

  • 48の情報源を体系的にレビューし,300以上の記述を34項目に分けました.
  • 71人の参加者が参加したワークショップで,学際的なシナリオ構築とゲーム開発の経験的テストを行いました.
  • 知識統合の規模を精査し,次元を特定するための探索的要素分析

主要な成果:

  • 分野を超えた研究における知識統合を測定するための25項目の精巧なスケールを開発した.
  • 知識統合の2つの重要な側面を特定しました. 社会情緒的および認知コミュニケーションの要因です.
  • 2つの異なった学際的な方法の知識統合の貢献を比較するために,スケールを成功裏に使用しました.

結論:

  • 開発されたスケールは,学際的な研究の有効性を測定するための方法論的改善を提供します.
  • このスケールは,学際的な方法の比較分析を可能にし,情報に基づいた選択をサポートします.
  • スケールの有用性と範囲を高めるため,さらに精細化することが推奨されています.