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Barry Markovsky - One of the best experts on this subject based on the ideXlab platform.

  • Graduate Training in Sociological Theory and Theory Construction
    Sociological Perspectives, 2020
    Co-Authors: Barry Markovsky
    Abstract:

    Nearly all of sociology's top graduate training programs require their students to complete one or two courses on sociological Theory. The instructors for these courses have an extraordinary opportunity to affect the perspectives and practices of future generations of scholars. This study assesses the backgrounds, attitudes, beliefs, and practices of those instructors regarding different approaches to theorizing, with particular attention paid to topics related to science and to Theory Construction. Soci- ologists who teach required Theory courses in the discipline's top fifty grad- uate training programs were asked a series of questions pertaining to their own training and to the courses they were teaching: attitudes toward dif- ferent kinds of theorizing, perceptions of the role that Theory plays in sociol- ogy and in science, and views on the nature of science. Results indicate a strong consensus on the most important classical theorists (Marx, Weber, and Durkheim). However, attitudes and practices varied widely in regard to other classical theorists, contemporary sociological Theory, and the role of scientific standards in the development of sociological knowledge. The author explores some of the implications of these attitudes and practices.

  • ACIT - Wikitheoria: A Computational Framework for Parsimonious Sociology Theory Construction
    Proceedings of the 7th ACIS International Conference on Applied Computing and Information Technology, 2019
    Co-Authors: Mingzhe Du, José M. Vidal, Barry Markovsky
    Abstract:

    In the social sciences, Theory Construction refers to the research process of building testable scientific theories to explain and predict observed phenomena in the natural world. Terms represent the theories' concepts or ideas and their meanings are explicated in their definitions. The principle of parsimony, an important criterion for evaluating the quality of theories (e.g., as exemplified by Occam's Razor) mandates that we minimize the number of definitions (terms) used in a given Theory. Conventional methods for parsimony analysis in Theory Construction are based on the heuristic approaches. However, it is not always easy for young researchers to understand the theoretical work in a given area because of the problem of "tacit knowledge", which often makes results lack coherence and logical integrity. Therefore, we propose a generic knowledge aggregation framework to facilitate the parsimonious approach of Theory Construction with a cloud-based Theory modularization platform and semantic-based algorithms to minimize the number of definitions. The proposed approach is demonstrated and evaluated using the modularized theories from the database and sociological definitions retrieved from the system lexicon and sociological literature. The experiment results showed that the proposed approach achieves the precision of 82%, recall of 82% and accuracy of 81.69%. This study proves the effectiveness of using cloud-based knowledge aggregation system and semantic analysis models for promoting the parsimonious sociology Theory Construction.

  • SOREC: A Semantic Content-Based Recommendation System for Parsimonious Sociology Theory Construction
    2019 IEEE Fifth International Conference on Big Data Computing Service and Applications (BigDataService), 2019
    Co-Authors: Mingzhe Du, José M. Vidal, Barry Markovsky
    Abstract:

    Theory Construction is the process of formulating scientific theories with reference to explicit logical and semantic criteria. Definitions and associated terms are essential components of the Theory, in which parsimony is a crucial criterion for Theory evaluation. The present work offers a novel semantic content-based recommendation system with supervised machine learning model for theoretical parsimony evaluation by checking the semantic consistency of definitions while constructing theories. Specifically, we evaluate the XGBoost tree-based classifier with the combination of 15 low-level features and 11 high-level features on our dataset. A sociologist annotated in-house dataset consisting of 2,235 definition pairs drawn from the sociological literature is used for evaluating the proposed methods. The experiment results showed that the proposed system achieves 86.16% accuracy, 84.42% F-measure and 86% precision in suggesting semantically related sociological definitions.

  • BigDataService - SOREC: A Semantic Content-Based Recommendation System for Parsimonious Sociology Theory Construction
    2019 IEEE Fifth International Conference on Big Data Computing Service and Applications (BigDataService), 2019
    Co-Authors: Mingzhe Du, José M. Vidal, Barry Markovsky
    Abstract:

    Theory Construction is the process of formulating scientific theories with reference to explicit logical and semantic criteria. Definitions and associated terms are essential components of the Theory, in which parsimony is a crucial criterion for Theory evaluation. The present work offers a novel semantic content-based recommendation system with supervised machine learning model for theoretical parsimony evaluation by checking the semantic consistency of definitions while constructing theories. Specifically, we evaluate the XGBoost tree-based classifier with the combination of 15 low-level features and 11 high-level features on our dataset. A sociologist annotated in-house dataset consisting of 2,235 definition pairs drawn from the sociological literature is used for evaluating the proposed methods. The experiment results showed that the proposed system achieves 86.16% accuracy, 84.42% F-measure and 86% precision in suggesting semantically related sociological definitions.

Mingzhe Du - One of the best experts on this subject based on the ideXlab platform.

  • ACIT - Wikitheoria: A Computational Framework for Parsimonious Sociology Theory Construction
    Proceedings of the 7th ACIS International Conference on Applied Computing and Information Technology, 2019
    Co-Authors: Mingzhe Du, José M. Vidal, Barry Markovsky
    Abstract:

    In the social sciences, Theory Construction refers to the research process of building testable scientific theories to explain and predict observed phenomena in the natural world. Terms represent the theories' concepts or ideas and their meanings are explicated in their definitions. The principle of parsimony, an important criterion for evaluating the quality of theories (e.g., as exemplified by Occam's Razor) mandates that we minimize the number of definitions (terms) used in a given Theory. Conventional methods for parsimony analysis in Theory Construction are based on the heuristic approaches. However, it is not always easy for young researchers to understand the theoretical work in a given area because of the problem of "tacit knowledge", which often makes results lack coherence and logical integrity. Therefore, we propose a generic knowledge aggregation framework to facilitate the parsimonious approach of Theory Construction with a cloud-based Theory modularization platform and semantic-based algorithms to minimize the number of definitions. The proposed approach is demonstrated and evaluated using the modularized theories from the database and sociological definitions retrieved from the system lexicon and sociological literature. The experiment results showed that the proposed approach achieves the precision of 82%, recall of 82% and accuracy of 81.69%. This study proves the effectiveness of using cloud-based knowledge aggregation system and semantic analysis models for promoting the parsimonious sociology Theory Construction.

  • SOREC: A Semantic Content-Based Recommendation System for Parsimonious Sociology Theory Construction
    2019 IEEE Fifth International Conference on Big Data Computing Service and Applications (BigDataService), 2019
    Co-Authors: Mingzhe Du, José M. Vidal, Barry Markovsky
    Abstract:

    Theory Construction is the process of formulating scientific theories with reference to explicit logical and semantic criteria. Definitions and associated terms are essential components of the Theory, in which parsimony is a crucial criterion for Theory evaluation. The present work offers a novel semantic content-based recommendation system with supervised machine learning model for theoretical parsimony evaluation by checking the semantic consistency of definitions while constructing theories. Specifically, we evaluate the XGBoost tree-based classifier with the combination of 15 low-level features and 11 high-level features on our dataset. A sociologist annotated in-house dataset consisting of 2,235 definition pairs drawn from the sociological literature is used for evaluating the proposed methods. The experiment results showed that the proposed system achieves 86.16% accuracy, 84.42% F-measure and 86% precision in suggesting semantically related sociological definitions.

  • BigDataService - SOREC: A Semantic Content-Based Recommendation System for Parsimonious Sociology Theory Construction
    2019 IEEE Fifth International Conference on Big Data Computing Service and Applications (BigDataService), 2019
    Co-Authors: Mingzhe Du, José M. Vidal, Barry Markovsky
    Abstract:

    Theory Construction is the process of formulating scientific theories with reference to explicit logical and semantic criteria. Definitions and associated terms are essential components of the Theory, in which parsimony is a crucial criterion for Theory evaluation. The present work offers a novel semantic content-based recommendation system with supervised machine learning model for theoretical parsimony evaluation by checking the semantic consistency of definitions while constructing theories. Specifically, we evaluate the XGBoost tree-based classifier with the combination of 15 low-level features and 11 high-level features on our dataset. A sociologist annotated in-house dataset consisting of 2,235 definition pairs drawn from the sociological literature is used for evaluating the proposed methods. The experiment results showed that the proposed system achieves 86.16% accuracy, 84.42% F-measure and 86% precision in suggesting semantically related sociological definitions.

José M. Vidal - One of the best experts on this subject based on the ideXlab platform.

  • ACIT - Wikitheoria: A Computational Framework for Parsimonious Sociology Theory Construction
    Proceedings of the 7th ACIS International Conference on Applied Computing and Information Technology, 2019
    Co-Authors: Mingzhe Du, José M. Vidal, Barry Markovsky
    Abstract:

    In the social sciences, Theory Construction refers to the research process of building testable scientific theories to explain and predict observed phenomena in the natural world. Terms represent the theories' concepts or ideas and their meanings are explicated in their definitions. The principle of parsimony, an important criterion for evaluating the quality of theories (e.g., as exemplified by Occam's Razor) mandates that we minimize the number of definitions (terms) used in a given Theory. Conventional methods for parsimony analysis in Theory Construction are based on the heuristic approaches. However, it is not always easy for young researchers to understand the theoretical work in a given area because of the problem of "tacit knowledge", which often makes results lack coherence and logical integrity. Therefore, we propose a generic knowledge aggregation framework to facilitate the parsimonious approach of Theory Construction with a cloud-based Theory modularization platform and semantic-based algorithms to minimize the number of definitions. The proposed approach is demonstrated and evaluated using the modularized theories from the database and sociological definitions retrieved from the system lexicon and sociological literature. The experiment results showed that the proposed approach achieves the precision of 82%, recall of 82% and accuracy of 81.69%. This study proves the effectiveness of using cloud-based knowledge aggregation system and semantic analysis models for promoting the parsimonious sociology Theory Construction.

  • SOREC: A Semantic Content-Based Recommendation System for Parsimonious Sociology Theory Construction
    2019 IEEE Fifth International Conference on Big Data Computing Service and Applications (BigDataService), 2019
    Co-Authors: Mingzhe Du, José M. Vidal, Barry Markovsky
    Abstract:

    Theory Construction is the process of formulating scientific theories with reference to explicit logical and semantic criteria. Definitions and associated terms are essential components of the Theory, in which parsimony is a crucial criterion for Theory evaluation. The present work offers a novel semantic content-based recommendation system with supervised machine learning model for theoretical parsimony evaluation by checking the semantic consistency of definitions while constructing theories. Specifically, we evaluate the XGBoost tree-based classifier with the combination of 15 low-level features and 11 high-level features on our dataset. A sociologist annotated in-house dataset consisting of 2,235 definition pairs drawn from the sociological literature is used for evaluating the proposed methods. The experiment results showed that the proposed system achieves 86.16% accuracy, 84.42% F-measure and 86% precision in suggesting semantically related sociological definitions.

  • BigDataService - SOREC: A Semantic Content-Based Recommendation System for Parsimonious Sociology Theory Construction
    2019 IEEE Fifth International Conference on Big Data Computing Service and Applications (BigDataService), 2019
    Co-Authors: Mingzhe Du, José M. Vidal, Barry Markovsky
    Abstract:

    Theory Construction is the process of formulating scientific theories with reference to explicit logical and semantic criteria. Definitions and associated terms are essential components of the Theory, in which parsimony is a crucial criterion for Theory evaluation. The present work offers a novel semantic content-based recommendation system with supervised machine learning model for theoretical parsimony evaluation by checking the semantic consistency of definitions while constructing theories. Specifically, we evaluate the XGBoost tree-based classifier with the combination of 15 low-level features and 11 high-level features on our dataset. A sociologist annotated in-house dataset consisting of 2,235 definition pairs drawn from the sociological literature is used for evaluating the proposed methods. The experiment results showed that the proposed system achieves 86.16% accuracy, 84.42% F-measure and 86% precision in suggesting semantically related sociological definitions.

Karl E. Weick - One of the best experts on this subject based on the ideXlab platform.

  • Theory Construction as disciplined reflexivity: Tradeoffs in the 90s
    Academy of Management Review, 1999
    Co-Authors: Karl E. Weick
    Abstract:

    The process of Theory Construction in organizational studies is portrayed as imagination disciplined by evolutionary processes analogous to artificial selection. The quality of Theory produced is pre- dicted to vary as a function of the accuracy and detail present in the problem statement that triggers Theory building, the number of and independence among the conjectures that attempt to solve the prob- lem, and the number and diversity of selection criteria used to test the conjectures. It is argued that interest is a substitute for validation during Theory Construction, middle range theories are a necessity if the process is to be kept manageable, and representations such as metaphors are inevitable, given the complexity of the subject matter.

Franziska Frankfurter - One of the best experts on this subject based on the ideXlab platform.

  • Sociological Theory Construction
    2020
    Co-Authors: Franziska Frankfurter
    Abstract:

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