Climate Change Considerations in Public Library Collection Development
Bibliographic record
Abstract
Climate change is one of the biggest threats to our continued existence. While resources and research about climate change are readily available, what do public libraries need to be doing, as one of the last free public spaces, to bridge the gap between complex information and community action? This extended abstract, based on a larger research paper, explores the various ways public libraries can be active members of their communities and promote conversations about climate change with the specific actions of their collection development. Through an in-depth literature search, several challenges are identified that act as barriers to creating cohesive, inclusive, and informative climate change-oriented collections. These barriers include eco-anxiety, particularly among children and young adults, environmental literacy, and climate change denial materials. Considering these challenges, recommendations are provided to overcome these obstacles. As the need for understanding and action becomes more dire, library policies and collection development strategies need to reflect those needs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.076 | 0.121 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.025 | 0.010 |
| Scholarly communication | 0.033 | 0.022 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 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".