Assessing Environmental Sustainability in Canadian University Libraries’ Strategic Plans
Bibliographic record
Abstract
Floods, famine, fires: fallout from the planet’s increasing temperature makes the climate crisis a reality that affects everyone (IPCC 2023) – libraries and librarians included. Academic libraries contribute to carbon emissions and waste through managing infrastructure, energy and water use, the purchasing of materials and resources, printing, and so on. While we are facing a dire situation, there is still plenty of room for action. Strategic plans are designed to provide direction and measurable goals which are essential to systematically furthering sustainability on campus. By exploring strategic plans of Canadian university libraries, our study provides an analysis of current strategic priorities and language which can be used to inform future strategic planning sustainability initiatives. The goal of this study is to explore the extent to which environmental sustainability is present in the strategic plans of Canadian university libraries, and to analyze how it is being included, when it is included at all. After a review of the literature, no analysis has been done on an institutional level regarding environmental sustainability (ES) presence in academic library strategic planning. Furthermore, much of the scholarly discourse regarding environmental sustainability in libraries is limited to surveys regarding participant perceptions, or communicating programming ideas related to environmental sustainability in academic libraries. This study aims to synthesize and communicate what is currently being done at the strategic planning level of academic libraries in Canada.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".