Bibliographic record
Abstract
Many librarians are inspired by Greta Thunberg and the millions of young people who have begun mobilizing to pressure government and corporate entities to address the climate crisis. During the Global Climate Strike week from September 20 to 27, 2019, it is estimated that over 7.5 million people worldwide joined Thunberg in agitating for change (Global Climate Strike, 2019). Our situation is dire. In June of 2019, scientists at the Permafrost Laboratory at the University of Alaska Fairbanks reported that permafrost melting in the Canadian High Arctic had already exceeded estimates of melting not previously expected to occur until the year 2090 (Farquharson et al., 2019). In response, Jennifer Morgan, executive director of Greenpeace International, stated that “thawing permafrost is one of the tipping points for climate breakdown and it’s happening before our very eyes” (Reuters, 2019). Rapid permafrost thawing would suddenly release enormous quantities of carbon dioxide and methane, initiating a feedback loop that could cause global temperature to rise even more catastrophically (Reuters, 2019). Recently, 400 scientists from 20 different countries released a statement urging mass actions of civil disobedience as the only way to pressure policy makers to act quickly enough in order to avert the worst consequences of climate change (Green, 2019).
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 | 0.021 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.022 | 0.007 |
| Scholarly communication | 0.031 | 0.031 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.015 | 0.011 |
| Insufficient payload (model declined to judge) | 0.119 | 0.103 |
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".