Words That Start With E: Why Librarians Should Fight Climate Change and Climate Change Denial
Bibliographic record
Abstract
Ecology, economy, equity. Exemplars, educators, enablers. Librarianship centres around the values ofcommunity-building, access to information, and advocating for the public good, and so librarians arepoised to be leaders when it comes to environmentally-friendly and sustainable practices and policies.Our commitment to intellectual freedom demands that we ensure facts about climate change reach thepublic, while social responsibility asks that we consider the harm that can be done by the spread ofdisinformation like climate change denial—the kind of harm that has led to the devastating, irreversiblecircumstances we’re in today. To ensure there will continue to be a community for libraries to serve,librarians must allow sustainability to underpin all their choices, especially with regard to educating thepublic, devaluing disinformation, and advocating for concrete collective action.
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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 | 0.009 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.028 |
| Scholarly communication | 0.023 | 0.032 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.023 | 0.020 |
| Insufficient payload (model declined to judge) | 0.030 | 0.019 |
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".