Since 2016, the Thai Government has pursued a twenty-year national economic growth policy, Thailand 4.0, promoting innovation and stimulating international investment through the Eastern Economic Corridor (EEC) project. The EEC project involves significant land acquisition resulting in the need to relocate villagers with potential impact on food security in a major food production area. This research explored the concerns of a local farming community regarding the potential loss of their farmland and means of livelihood under the EEC project using a case study in Ban Pho District of Chachoengsao (CCS) province. It described their resulting action to protect their farmland using community organizing. Data was collected through documents, observation and semi-structured interviews of key stakeholders. The results demonstrate the role of social capital in community organizing. We contend that high social capital stock is a necessary precursor to create conditions for community members to take steps to defend and protect their interests. This paper contributes to a deeper understanding of the role of social capital in community organizing in cases involving natural resource management.
Background: The air traffic management (ATM) system has historically coped with a global increase in traffic demand ultimately leading to increased operational complexity. When dealing with the impact of this increasing complexity on system safety it is crucial to automatically analyse the loss of separation (LoS) using tools able to extract meaningful and actionable information from safety reports. Current research in this field mainly exploits natural language processing (NLP) to categorise the reports, with the limitations that the considered categories need to be manually annotated by experts and that general taxonomies are seldom exploited. Methods: To address the current gaps, authors propose to perform exploratory data analysis on safety reports combining state-of-the-art techniques like topic modelling and clustering and then to develop an algorithm able to extract the Toolkit for ATM Occurrence Investigation (TOKAI) taxonomy factors from the free-text safety reports based on syntactic analysis. TOKAI is a general taxonomy developed by EUROCONTROL and intended to become a standard and harmonised approach to future investigations. Results: Leveraging on the LoS events reported in the public databases of the Comisión de Estudio y Análisis de Notificaciones de Incidentes de Tránsito Aéreo and the United Kingdom Airprox Board, authors show how their proposal is able to automatically extract meaningful and actionable information from safety reports and to classify them according to the TOKAI taxonomy. The quality of the approach is also indirectly validated by checking the connection between the identified factors and the main contributor of the incidents. Conclusions: Authors' results are a promising first step toward the full automation of a general analysis of LoS reports supported by results on real world data coming from two different sources. In the future, authors' proposal could be extended to other taxonomies or tailored to identify factors to be included in the safety taxonomies.
Background: The air traffic management (ATM) system has historically coped with a global increase in traffic demand ultimately leading to increased operational complexity. When dealing with the impact of this increasing complexity on system safety it is crucial to automatically analyse the losses of separation (LoSs) using tools able to extract meaningful and actionable information from safety reports. Current research in this field mainly exploits natural language processing (NLP) to categorise the reports,with the limitations that the considered categories need to be manually annotated by experts and that general taxonomies are seldom exploited. Methods: To address the current gaps,authors propose to perform exploratory data analysis on safety reports combining state-of-the-art techniques like topic modelling and clustering and then to develop an algorithm able to extract the Toolkit for ATM Occurrence Investigation (TOKAI) taxonomy factors from the free-text safety reports based on syntactic analysis. TOKAI is a tool for investigation developed by EUROCONTROL and its taxonomy is intended to become a standard and harmonised approach to future investigations. Results: Leveraging on the LoS events reported in the public databases of the Comisión de Estudio y Análisis de Notificaciones de Incidentes de Tránsito Aéreo and the United Kingdom Airprox Board,authors show how their proposal is able to automatically extract meaningful and actionable information from safety reports,other than to classify their content according to the TOKAI taxonomy. The quality of the approach is also indirectly validated by checking the connection between the identified factors and the main contributor of the incidents. Conclusions: Authors' results are a promising first step toward the full automation of a general analysis of LoS reports supported by results on real-world data coming from two different sources. In the future,authors' proposal could be extended to other taxonomies or tailored to identify factors to be included in the safety taxonomies.
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