(The Problem with) UN SDGs as a Measure of Sustainability in Academic Libraries, and an Exploration of Alternatives
Bibliographic record
Abstract
Year over year, we are continuing to observe the impacts of the climate crisis worsen, from floods, fires (lest we forget the persistent air quality advisory from wildfire smoke across Canada and the United States in summer 2023) and storms, to climate refugees and climate anxiety. As Dr. Kimberly Nicholas (2021) so succinctly frames the climate crisis: “It’s warming. It’s us. We’re sure. It’s bad. We can fix it.” In order to fix it, we all have a role to play – especially academic libraries. All jobs are climate jobs—academic library professionals need to respond to the call for action to reflect on their professional scope of influence to determine the ways in which we can have an impact and push our institutions towards meaningful change. In 2015, the United Nations adopted the 2030 Agenda for Sustainable Development, which centres 17 goals meant to simultaneously recognize and inspire action on targets related to health, education, inequality, economic growth, and climate change. These United Nations Sustainable Development Goals (UN SDGs) categorize and rank progress within and between countries based on the 17 goals. This paper seeks to address the question of how to assess sustainability in academic libraries. While the UN SDGs are one of, if not the most, prominent forms of thinking about sustainable assessment in higher education, the inadequacy of the UN SDGs should cause pause and prompt consideration of alternative measures of sustainability in academic libraries. I aim to point in the direction of other alternative forms of sustainable assessment that may be more productively deployed in the context of academic libraries that address the gaps found in the UN SDGs, such as Indigenous methodologies, the Sustainability Tracking, Assessment & Rating System (STARS), the Sustainable Libraries Initiative (SLI) or true cost assessment.
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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 | 0.081 | 0.246 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.006 | 0.032 |
| Scholarly communication | 0.031 | 0.041 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.010 | 0.025 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".