Bibliographic record
Abstract
In response to the COVID-19 pandemic, governments around the world are mobilizing unprecedented public resources to mitigate economic collapse. However, these new programs run the risk of paying insufficient attention to the multiple sustainability crises we face. Climate change, in particular, threatens the very basis for continued human prosperity and requires an equal, if not greater, societal mobilization. In this policy brief, we argue that the response to the coronavirus outbreak also offers an opportunity to advance the climate agenda. Indeed, given that we have scarce resources at our disposal, it is essential that we synergize such efforts. We propose that this can be accomplished in two primary ways: (1) harnessing the disruptive forces of the COVID-19 pandemic to accelerate the decline of carbon-intensive industries, technologies, and practices, and (2) leveraging responses to drive low-carbon innovation. From these two strategies, we outline five principles of “sustainability transition policy” to serve as a guide during these challenging times.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.016 | 0.021 |
| Insufficient payload (model declined to judge) | 0.010 | 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".