Climate Research Made Real : Practical Applications of Research towards Better Futures
Bibliographic record
Abstract
In the December 5, 2019 Declaration on the Climate Emergency, the UBC Board of Governors stated that the University has a mandate “to effect change beyond our institutional boundaries” and to “advance a sustainable and just society across British Columbia, Canada and the world.” In response to this mandate, UBC Library's L#CAT (Library Climate Act Team) organized the panel event, “Climate Research Made Real: Practical Applications of Research for Better Futures”. Moderated by Kathryn Harrison, Professor in the Department of Political Science, panelists include Amanda Giang, Assistant Professor in the Institute for Resources, Environment and Sustainability and the Department of Mechanical Engineering; Maggie Low, Assistant Professor in the School of Community and Regional Planning; and Rita Wong, Associate Professor in the Faculty of Culture and Community at the Emily Carr University of Art + Design. Many thanks to Sustainability & Engineering | Campus + Community Planning for awarding UBC Library a UBC Workplace Sustainability Fund grant in support of this event.
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 | 0.091 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.032 |
| Scholarly communication | 0.022 | 0.027 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.042 | 0.007 |
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".