The Role of Special Collections in Climate Change Movements
Bibliographic record
Abstract
This presentation examines the role that special collections can play in documenting climate change literature over the course of history. We chose to focus on artist books in particular due to their unique trait as a format found almost exclusively within special collections libraries. We connect artist books to other collections within Bruce Peel Special Collections to create a narrative of climate change across cultures, time, and social movements. This narrative focuses on the past, present, and future of climate change, showcasing how special collections libraries provide new perspectives on how we should view the role we have to play in climate change. We believe the creation of this narrative shows how collecting literature and archives is an active form of combatting climate misinformation. In particular, we believe connecting this narrative through artist books is a way to challenge dominant narratives due to the role of artist books in counterculture and alternate ways of viewing knowledge.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.035 | 0.028 |
| Scholarly communication | 0.032 | 0.018 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.003 | 0.005 |
| 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".