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. The same logic that demands a broadened strategy of engagement to deliver a more robust global response to climate change also suggests a parallel need to rethink international law to involve subnational actors whose participation may be required to deliver a successful outcome on some issues. Specifically, the global response to problems that go beyond the authority or capacity of national governments—including but not limited to climate change—would be strengthened by creating new international legal mechanisms that formalize the action commitments of mayors, governors, premiers, and CEOs. Giving these officials a way to formally signal their commitment to the success of the Paris Agreement on the international plane would help deepen the global response to the problem.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".