Creating space for climate justice in library and information science
Bibliographic record
Abstract
Introduction. We already live with the consequences of climate change, although such changes are experienced by humans, non-humans and the more-than-human world in vastly different ways, even within the same geographical regions. Climate change underpins, intersects and is the context in which our everyday lives and our work takes place. While libraries and library organisations have been discussing and addressing climate change for years, in this paper, we advocate for the field of library and information science (IS/LIS) to directly acknowledge climate change and create space for climate justice across our teaching, research and practice. Method. Building from our own experiences in these areas, we offer four entry points to provide examples and inspiration for IS/LIS researchers, educators and practitioners to consider climate justice in their work by: (1) investigating connections between informational and environmental injustices, (2) exploring intersections among heritage, memory and cultural climate justice; (3) disaster planning and pedagogy, and (4) imagining aspirational futures. Results and Conclusions. Using these four entry points to create space for climate justice in IS/LIS, we offer three propositions: embed climate justice across the IS/LIS curriculum, develop a climate justice research stream, and collaborate across sectors to build community and to imagine just alternative futures.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.028 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 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".