Open for Climate Justice: To Solve the World’s Biggest Problems We Need Open Knowledge
Bibliographic record
Abstract
The University Libraries virtually hosted Dr. Monica Granados (she/her), the Climate Change Campaign Manager for Creative Commons, as a speaker on the first day of Open Access Week, October 24th, 2022 at noon Mountain Standard Time. “Open for Climate Justice” was the theme for 2022's International Open Access Week. This theme sought to encourage connection and collaboration between the climate movement and the international open community. Tackling the climate crisis requires the rapid exchange of knowledge across geographic, economic, and disciplinary boundaries. Dr. Granados has a PhD in ecology from McGill University. While working on her PhD, Monica discovered that incentives in academia promote practices that make knowledge less accessible. Since then, Monica has devoted her career to working in the open science space in pursuit of making knowledge more equitable and accessible. As a Senior Policy Advisor at Environment and Climate Change Canada she provided subject matter expertise and supported the delivery of open science in the Government of Canada. Monica is now working at Creative Commons on a global campaign promoting open access of climate and biodiversity research. As a member of the Leadership Team at PREreview she works to make peer review more open and diverse. She is also on the Board of Directors of the Canadian Open Data Society promoting open data in Canada and alumna of the Frictonless Data Fellowship.
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 | 0.028 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.022 | 0.045 |
| Scholarly communication | 0.030 | 0.051 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.010 | 0.023 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".