Sustainability in Library Collection Development
Bibliographic record
Abstract
The topic of sustainability generates keen interest in many contemporary spheres. As vital partners in the publishing ecosystem, academic libraries are implicated in the push to make the content that supports teaching and research more equitable and sustainable. This paper examines the relationship between the practices of academic publishers and the sustainability goals of academic libraries related to collection management, through the lens of a Green Audit featuring a customized rubric to facilitate the assessment of the green practices of publishers. The research team assessed the environmental practices, current impact, and future commitments of publishers, with an anticipated outcome of reducing their library’s carbon footprint and advancing sustainable collection practices. Findings are drawn from an audit of sixteen international academic publishers that examined material elements as well as transportation and infrastructure considerations. Audit results reveal an uneven picture of the industry, owing primarily to funding levels and staffing capacity.
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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 | 0.025 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.033 | 0.015 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".