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Abstract The United Nations’ 2030 Agenda for Sustainable Development provides a shared blueprint for peace and prosperity for people and the planet using 17 interlinked global goals commonly known as the Sustainable Development Goals (SDGs). Mapping research publications in concordance with the SDGs is important in understanding research activities and intensities in supporting each of the SDGs at global, country, institutional, and individual levels. Such mapping is challenging as what research outcomes and innovations belong to a specific SDG category is subject to interpretation and can be highly complex and contextual. Several SDG mapping approaches have been proposed and tested by organisations and researchers, but tend not to produce consistent mapping results. In this study, an enhanced method to conductSDG research mapping, Auckland Approach, is proposed based on the pioneering SDG mapping work conducted by Elsevier, Sustainable Development Solutions Network (SDSN), and United Nations (UN). The Auckland Approach uses text-mining techniques and n-gram analyses to extract global and local SDG keywords from publication metadata (i.e. title, keywords, and abstracts). The keywords are sorted according to the number of publications that contain the terms, and the keywords’ term frequency–inverse document frequency (tf-idf) values, respectively. After result validation, the top-ranked terms (i.e., top 200) are manually reviewed to retain keywords that are meaningful and relevant. The selected keywords are combined with those adopted by Elsevier, SDSN, and UN SDG Indicators to form our final SDG keyword lists, and the corresponding bibliometric queries are compiled. The enhanced Auckland Approach captures a significant number of unique research publications that are SDG-related and contextually relevant. Our approach promotes SDG mapping that is data-driven, transparent, comprehensive, and contextualised. The Auckland Approach also provides an example of forming local SDG narratives and themes from a generic, global approach to make a research mapping more relevant to its local communities and research priorities.
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Stored only when both papers are in the corpus — 8 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.