Digital artifacts reveal development and diffusion of climate research

Abstract

Research for development organizations generate tremendous amount of accessible knowledge, but given their scale, time and resource constraints, the impact of outputs is not systematically analyzed. This is because traditional bibliometric analyses present limitations to synthesize accumulated knowledge and retrofitting indicators to historical outputs. To address these shortcomings, this study proposes an integrated, web-based approach to systematically analyze the production and diffusion of knowledge from large-scale research programs, using climate research of the International Maize and Wheat Improvement Center (CIMMYT) as a case study. Our analytical framework employs text mining, social network analysis and hyperlink analysis to an unstructured mass of publicly available digital artifacts such as institutional repositories, citation databases, and social media to uncover narratives, dynamics, and relationships. Findings show CIMMYT’s climate research is strongly incorporated into a holistic systems approach and that the institution is actively engaged in knowledge exchanges with key actors from the scientific, development and public policy communities. The proposed analytical framework establishes an effective approach for research for development organizations to leverage existing online data sources to assess the extent of their knowledge production, dissemination, and reach.

Institutional Context of Pest Management Science in the Global South

“…While pest-centric research in laboratory ‘microworlds’ is crucial for organismal identification or biosecurity, there are concerns that such research may narrow mindsets, slacken the flow of information, or hamper our understanding of ‘real-world’ ecological processes [ 73 ]. Though development cooperation could incrementally improve wholeness-oriented research, institutions such as the CGIAR are commended for actively investing in interdisciplinary, solution-oriented research, e.g., by methodically assessing the scope and societal reach of scientific thrusts [ 74 , 75 ]. We equally argue that the embryonic approach of hierarchical stratification constitutes a valuable approach to strategize science and guide institutional reform, being well suited to transcend nature–society dualisms and integrate resilience concepts [ 16 , 47 ].…”

5th International Conference on Advanced Research Methods and Analytics (CARMA 2023)

“…In total, 49,335 Tweets were collected between 2012-09-13 to 2022-05-26. Carneiro et al (2022), in which topics were matched to AGROVOC1, the Food and Agriculture Organization's (FAO) open-source, multilingual vocabulary. For each topic, the corresponding AGROVOC concept was extracted, and a custom algorithm was developed to detect and classify the related terminology within the text of Tweets.…”

“…While extensive research about climate change discourses on social media have been conducted, focusing on various subjects such as issue polarization, disinformation, activism, and climate communication (see Pearce et al 2019 andFalkenberg et al 2022, among many others), this study investigates this study relies on the foundations of Digital Methods (Rogers 2013;Carneiro et al 2022) to explore climate security narratives and dynamics among policy actors. An online issue mapping approach (Rogers et al 2015) was applied to investigate two main questions: 1) How salient are climate security issues in national policy agendas?…”

“…Extracting knowledge from extensive text-based sources poses several challenges (Garbero et al 2021). Conventional approaches such as manual coding or keyword searches time and resource intensive (Carneiro et al 2022). This is the reason why we propose a text mining approach: automated retrieval of high-quality information from text, by finding patterns and trends through machine learning, statistics and linguistics .…”

Food insecurity trends in the Famine Early Warning Systems Network

“…Extracting knowledge from extensive text-based sources poses several challenges (Garbero et al 2021). Conventional approaches such as manual coding or keyword searches time and resource intensive (Carneiro et al 2022). This is the reason why we propose a text mining approach: automated retrieval of high-quality information from text, by finding patterns and trends through machine learning, statistics and linguistics (Resce, Maynard, 2018).…”