The Importance of Being Factual: The U.S., China, and the Future of the Kyoto Protocol
Bibliographic record
Abstract
By most accounts, the December 2012 Doha Round negotiations achieved little. The continued failure of member governments to reach consensus increases the risk of a catastrophic rise in global emissions. The current impasse is due in no small measure to the expressed concern of the United States that a climate change treaty will end up transferring enormous wealth from the United States to China. Analyzing the relevant market data, this Article concludes that there is little or no evidence to support the notion that ratification of the Kyoto Protocol will lead to the massive wealth transfers feared by the United States. Indeed, the market study demonstrates the opposite. By deconstructing the “China myth,” this Article achieves two tasks. First, it rebuts the principal argument that U.S. policy-makers and the Senate have offered to justify the United States’ refusal to ratify the Kyoto Protocol. Second, in taking China out of the equation, it enables U.S. climate justice theory to resume the arrested conversation about the obligations of the United States, and other developed nations, to address the problem of global emissions.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.036 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".