On the use of computer‐assistance to facilitate systematic mapping

Abstract

The volume of published academic research is growing rapidly and this new era of "big literature" poses new challenges to evidence synthesis, pushing traditional, manual methods of evidence synthesis to their limits. New technology developments, including machine learning, are likely to provide solutions to the problem of information overload and allow scaling of systematic maps to large and even vast literatures. In this paper, we outline how systematic maps lend themselves well to automation and computer-assistance. We believe that it is a major priority to consolidate efforts to develop and validate efficient, rigorous and robust applications of these novel technologies, ensuring the challenges of big literature do not prevent the future production of systematic maps.

Systematic mapping of global research on climate and health: a machine learning review

“…Second, exponentially increasing literature means that conventional evidence synthesis methods that typically require considerable human resources to manually collate and screen literature are no longer sufficient or feasible. 18 , 19 , 20 Indeed, faced with this dilemma, many evidence syntheses have responded by narrowing their review focus, reviewing an increasingly smaller portion of the literature, and further compromising the potential for broader insights across disciplinary silos. 1 , 20 , 21 …”

Global Tracking of Climate Change Adaptation Policy Using Machine Learning: a Systematic Map Protocol

“…Some climate change researchers have used machine learning approaches to build and analyse large text-based datasets (Hsu and Rauber, 2021, Lesnikowski et al, 2019a, Biesbroek et al, 2020, Callaghan et al, 2020, Lamb et al, 2019. Relatedly, the phenomenon of "Big Literature" (Nunez-Mir et al, 2016), a rapid increase in the number of scienti c publications, has led some within the evidence synthesis community to integrate machine learning methods into a more traditional systematic review process (Haddaway et al, 2020, Nakagawa et al, 2019, Van de Schoot et al, 2021. This includes the use of machine learning to create "evidence maps"; for example, to identify trends and research gaps on health impacts of climate change (Berrang-Ford et al, 2021c), to assess progress within climate change adaptation research (Sietsma et al, 2021) or to categorise and estimate the size of climate change impacts worldwide (Callaghan et al, 2021).…”

A systematic global stocktake of evidence on human adaptation to climate change

“…As the volume of literature makes reliable synthesis via conventional assessment methods impossible, we draw on two recent approaches in information science: machine learning [38][39][40] and collaborative networks [41][42][43][44] . Machine learning techniques allow us to rapidly sort thousands of documents and capture the breadth of adaptation literature to an extent that would not be feasible using manual methods 32,36,37,39,40,45 . We used supervised machine learning to screen 48,816 articles published between 2013 and 2019 and identified 1,682 articles that met our inclusion criteria (Methods and Extended Data Figs.…”

“…44 Faculty of Agronomic Sciences, University of Abomey-Calavi, Cotonou, Benin. 45 CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Bamako, Mali. 46 Australian National Centre for the Public Awareness of Science, The Australian National University, Canberra, Australian Capital Territory, Australia.…”