Bibliographic record
Abstract
Since the 1970s, the climate community has worked tirelessly to establish a credible scientific basis for anthropogenic climate change. Though climate change deniers still exist, ever since the International Panel for Climate Change (the IPCC) declared that “observed increase in global average temperatures since the mid-20th century is very likely due to the observed increase in anthropogenic greenhouse gas concentrations,” there has been increasing global recognition of the issue.1 Yet, this global consensus has not directly led to the implementation of a singular global climate policy, but rather to several fragmented international agreements each varying in degree of success. These agreements have all failed to adequately address the entire issue, and with the absence of significant international action, the planet is now on track to warm by at least 2.5 degrees this century.2 Thus, I seek to investigate the conditions that explain this drastic variation in success. After examining the cases of both a successful climate deal, the Montreal Protocol, and a widely considered failed climate deal, the Kyoto Protocol, I will argue that there is one key method for obtaining a successful climate deal: a “carrots and sticks” approach, including binding emission reductions as well as an enforcement mechanism to incentivize them.
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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.044 | 0.007 |
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".