POLICYFORUM P rices in the European Union’s (EU) Emissions Trading Sys-
Bibliographic record
Abstract
historic lows. Elected offi cials have promised to repeal the Australian carbon market. Yet fi ve new regional carbon markets recently began in China, which nearly doubled the volume of emissions covered by trading programs. This follows Cali-fornia’s successful launch of its cap-and-trade program in 2013 and its 2014 link to Quebec’s market. Are carbon markets seriously challenged or succeeding and on the rise? Sixteen years after the Kyoto Protocol was signed and the idea of emis-sions trading emerged as a dominant policy paradigm, we have learned much about what makes carbon markets work—and what does not. New questions are emerging for research-ers and policy-makers, including how carbon markets fi t into a complex global framework. Think Global, Act Local? A truly global trading program is as yet unlikely, if not impossible. Because a unit of carbon dioxide (CO2) emitted anywhere has a uniform impact on global climate, a single global market would be economically desir-able, equalizing incentives to reduce emis-sions everywhere. In practice, we see a mul-tiplicity of multinational, national, and sub-national markets—including the European
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.052 | 0.011 |
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".