Examples
Worked examples
- Is an instance
A university uses the published Aurora Universities Network SDG-mapping queries to run its publication corpus against Scopus or Web of Science search syntax, tagging matched outputs by SDG.
- Is an instance
Elsevier's SciVal platform applies its own documented SDG queries to classify a submitted institution's publication set for reporting purposes.
Counter-examples
Looks similar, but isn't
- Not an instance
Manually assigning an SDG label to an output based on a researcher's own subjective sense of relevance, with no reference to a documented or published query methodology, is not what this term describes -- though it is a common informal practice that SDG-mapping methodologies are partly designed to replace with something checkable.
Editorial commentary
SDG mapping uses keyword-based, citation-based, or machine-learning approaches to classify publications, datasets, and projects against the Sustainable Development Goals. Several major projects have published their own SDG-mapping queries or methodologies: Elsevier’s SciVal SDG queries, the Aurora Universities Network’s openly published search queries, the SDG Publishers Compact Fellows initiative, and the methodology underlying Times Higher Education’s Impact Rankings, each of which takes a somewhat different approach to the same underlying classification problem.
Why mapping quality varies
Classification quality varies for several structural reasons: keyword-based queries can miss relevant context or match irrelevant outputs on shared vocabulary alone (a paper mentioning “poverty line” in an unrelated econometrics context, for instance, may be mis-tagged against SDG 1); classification schemes are not standardised across providers, so the same output can be mapped differently depending on which methodology is applied; and a single output very often genuinely contributes to multiple goals simultaneously, which flat, single-label classification schemes handle poorly.
Recommended practice
Because of these limitations, documented best practice is to use a transparent, published query methodology rather than an undocumented or proprietary black-box classifier; to allow manual review and override of automated classifications rather than treating the output as final; and to present SDG mapping results as exploratory and directional — useful for portfolio-level trend reporting — rather than as a definitive, individually authoritative statement about any single output’s societal relevance.
References
- Aurora SDG mapping queries (github.com/aurora-network-global/sdg-queries). Elsevier SciVal SDG documentation. Vanderfeesten M et al. ‘Search queries for mapping the SDGs to research.’ Zenodo, 2020.
Also known as
SDG classification · SDG tagging
Machine-readable encodings
Use in your systems
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