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Globalization of agriculture via the evergreen revolution (which encompasses large-scale production-collection-cleaning-processing-packaging-transportation-storage-distribution-sale of high-value cereals-fruits-flowers-vegetables-agrofuel-feedstock through technology-intensive global value chains) has opened the door to corporate capital involvement in agriculture. While the mainstream perspectives and international organizations have optimistically viewed this as a catalyst for inclusive growth, this article seeks to unveil the concealed hegemony of capital underlying the ostensibly beneficial façade of the evergreen revolution. It underscores the concerns regarding the immiseration of asset-poor farmers, petty nonfarm entrepreneurs and labourers resulting from the globalization of agriculture. Furthermore, it explores the implications for micro and macro food security in this context.

Interaction content is created during online learning interaction for the exchanged information to convey experience and share knowledge. Prior studies have mainly focused on the quantity of online learning interaction content (OLIC) from the perspective of types or frequency, resulting in a limited analysis of the quality of OLIC. Domain concepts as the highest form of interaction are shown as entities or things that are particularly relevant to the educational domain of an online course. The purpose of this paper is to explore a new method to evaluate the quality of OLIC using domain concepts.

This paper proposes a novel approach to automatically evaluate the quality of OLIC regarding relevance, completeness and usefulness. A sample of OLIC corpus is classified and evaluated based on domain concepts and textual features.

Experimental results show that random forest classifiers not only outperform logistic regression and support vector machines but also their performance is improved by considering the quality dimensions of relevance and completeness. In addition, domain concepts contribute to improving the performance of evaluating OLIC.

This paper adopts a limited sample to train the classification models. It has great benefits in monitoring students’ knowledge performance, supporting teachers’ decision-making and even enhancing the efficiency of school management.

This study extends the research of domain concepts in quality evaluation, especially in the online learning domain. It also has great potential for other domains.