Bibliographic record
Abstract
Is the 2015 Paris Agreement on climate change destined to succeed or doomed to fail? If all the pledges embedded in the intended nationally determined contributions (INDCs) are implemented fully, temperatures at the Earth’s surface are predicted to rise by 3–4 °C, far above the agreement’s goal of limiting increases to 1.5 °C. This means that the fate of the agreement will be determined by the success of efforts to strengthen or ratchet up the commitments contained in the national pledges over time. The first substantive section of this essay provides a general account of mechanisms for ratcheting up commitments and conditions determining the use of these mechanisms in international environmental agreements. The second section applies this analysis to the specific case of the Paris Agreement. The conclusion is mixed. There are plenty of reasons to doubt whether the Paris Agreement will succeed in moving from strength to strength in a fashion resembling experience with the Montreal Protocol on ozone depleting substances. Nevertheless, there is more room for hope in this regard than those who see the climate problem as unusually malign, wicked, or even diabolical are willing to acknowledge.
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 | 0.020 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.019 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".