Are Academic Libraries Doing Enough to Support the Sustainable Development Goals (SDGs)? A Mixed-Methods Review
Bibliographic record
Abstract
Objective – The goal of this study was to assess global academic libraries' role and activities aimed at achieving Sustainable Development Goals (SDGs). The paper highlights the enablers and barriers encountered in SDG programming and identifies future directions of SDG research in academic and other types of libraries. Methods – A mixed-methods review was conducted to address the research question: How do academic libraries contribute to the attainment of SDGs? The methodology included literature searches conducted in Scopus, Web of Science Core Collection, EBSCO’s Library, Information Science & Technology Abstracts (LISTA), and hand-searching. The selected timeframe, 2017-2024, encompasses the introduction of the SDGs and extends to the present body of evidence. Results – The study found 25 relevant articles with data from 164 academic libraries worldwide. The evidence base indicates limited awareness and examples of sustainability literacy, suggesting the need for new initiatives. Instances of "SDG washing" were identified where librarians exaggerated the impact of their SDG-related programs, mislabeled routine activities as SDG contributions, or used SDG terminology superficially without meaningful action. This study suggests that SDG attainment is influenced by leadership, organizational culture, personal initiatives, and partnerships. Conclusions – Academic libraries simultaneously address multiple SDG targets, indicating a comprehensive sustainability approach. Positive correlations between specific targets imply synergies that libraries can exploit to strengthen their sustainable development roles. Future research should investigate the impact of institutional factors on SDG implementation in academic libraries and identify strategies to overcome the common challenges in SDG initiatives. Specific SDG targets and indicators should guide context-specific recommendations. It is also advised to develop standardized tools for measuring and comparing academic libraries' SDG contributions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.045 | 0.123 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.023 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".