Bibliographic record
Abstract
After more than two decades of inadequate international efforts to address climate change resulting from rising greenhouse gas emissions, the 2015 Paris Climate Change Agreement shifted gears. That agreement advances a “bottom-up” model of global cooperation that requires action commitments from all national governments and acknowledges the important role that cities, states, provinces, and businesses must play in delivering deep decarbonization. Given the limited control that presidents and prime ministers have over many of the policies and choices that determine their countries’ carbon footprints, the Paris Agreement missed an opportunity to formally recognize the climate change action commitments of mayors, governors, and premiers. These subnational officials often have authorities complementary to national governments, particularly in federal systems (including the United States, China, Canada, and Australia). They therefore possess significant independent capacities to reduce greenhouse gas emissions through their economic development strategies, building codes, zoning rules and practices, public transportation investments, and other policies. Likewise, the world community missed an opportunity to formally recognize the commitments of companies to successful implementation of the Paris Agreement and thereby to highlight the wide range of decisions that business leaders make that significantly affect greenhouse gas emissions.
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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.018 | 0.025 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".