A deep learning classification framework for research methods of marine protected area management DOI Creative Commons
Mingbao Chen, Zhibin Xu

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 368, P. 122228 - 122228

Published: Aug. 24, 2024

The latest emerging transdisciplinary marine protected area (MPA) research scheme requires efficient approaches for theoretically based and data-driven method integration. However, due to the rapid development diversification of methods, it is growingly difficult locate new methods in methodological dimensions integrate them utmost utility. This study proposes a deep learning-based classification framework MPA management focused particularly on data theory capabilities using natural language processing (NLP). It extracted keywords from academic sources performed clustering semantic similarity, generating benchmark texts abstract labeling. By training learning NLP model analyzing abstracts 9049 empirical articles 1986 2024, scores were attributed each article, total 19 major categories 110 segment branches identified qualitative, quantitative, mixed genres. Combination types summarized, yielding data-theory neutralization principle where average tend approximate 0.50. Applying broadens traditional boundaries integration extends synthesis higher numbers, practical 2paradigm future research. Implications include bridging social ecological data, theorizing emergent challenges complex systems integrating construction science. applicable quantification other environmental disciplines can serve as guidance multidisciplinary © 2017 Elsevier Inc. All rights reserved.

Language: Английский

The quiet voices of French territories in tuna fisheries management. DOI Creative Commons

Rambourg Constance,

Haas Bianca,

Chantal Mathieu

et al.

Environmental Development, Journal Year: 2025, Volume and Issue: unknown, P. 101162 - 101162

Published: Feb. 1, 2025

Language: Английский

Citations

0
Integrating equity and justice in marine ecosystem models: An incremental but meaningful approach DOI Creative Commons
Sieme Bossier, Andrés M. Cisneros‐Montemayor

Ecological Modelling, Journal Year: 2025, Volume and Issue: 503, P. 111058 - 111058

Published: Feb. 17, 2025

Language: Английский

Citations

0
A deep learning classification framework for research methods of marine protected area management DOI Creative Commons
Mingbao Chen, Zhibin Xu

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 368, P. 122228 - 122228

Published: Aug. 24, 2024

The latest emerging transdisciplinary marine protected area (MPA) research scheme requires efficient approaches for theoretically based and data-driven method integration. However, due to the rapid development diversification of methods, it is growingly difficult locate new methods in methodological dimensions integrate them utmost utility. This study proposes a deep learning-based classification framework MPA management focused particularly on data theory capabilities using natural language processing (NLP). It extracted keywords from academic sources performed clustering semantic similarity, generating benchmark texts abstract labeling. By training learning NLP model analyzing abstracts 9049 empirical articles 1986 2024, scores were attributed each article, total 19 major categories 110 segment branches identified qualitative, quantitative, mixed genres. Combination types summarized, yielding data-theory neutralization principle where average tend approximate 0.50. Applying broadens traditional boundaries integration extends synthesis higher numbers, practical 2paradigm future research. Implications include bridging social ecological data, theorizing emergent challenges complex systems integrating construction science. applicable quantification other environmental disciplines can serve as guidance multidisciplinary © 2017 Elsevier Inc. All rights reserved.

Language: Английский

Citations

1