A deep learning classification framework for research methods of marine protected area management.

Area of Science:

• Marine Protected Area (MPA) management
• Environmental Science
• Data Science
• Computational Social Science

Background:

• Integrating diverse research methods in marine protected area (MPA) management is challenging due to rapid methodological advancements.
• Existing approaches struggle to efficiently categorize and synthesize data-driven and theory-based research methods.

Purpose of the Study:

• To develop a deep learning-based classification framework for marine protected area (MPA) management methods.
• To quantify the data and theory components of empirical MPA research using natural language processing (NLP).
• To establish a principle for method integration and synthesis in transdisciplinary environmental research.

Main Methods:

• Utilized natural language processing (NLP) and deep learning to analyze 9049 MPA management research abstracts (1986-2024).
• Extracted keywords, performed semantic clustering, and trained a model to assign data and theory scores.
• Identified 19 major method categories and 110 segment branches across qualitative, quantitative, and mixed methods.

Main Results:

• Attributed data and theory scores to each research article, revealing a 'data-theory neutralization principle' where scores average around 0.50.
• Classified 19 major method categories and 110 segment branches within MPA management research.
• Demonstrated the framework's applicability to quantify and integrate methods in environmental management.

Conclusions:

• The proposed framework facilitates efficient integration of theoretically based and data-driven methods in MPA management.
• The 'data-theory neutralization principle' offers a new paradigm for synthesizing diverse research approaches.
• This approach bridges social and ecological data, aids in theorizing complex systems, and supports multidisciplinary method integration.