Global imbalances in climate protection, leadership ambitions and EU climate change law
Bibliographic record
Abstract
The message conveyed by the Intergovernmental Panel on Climate Change (IPCC) is clear: the global climate crisis has arrived (e.g., IPCC, 2007, 2014). It is commonly understood that responding to this crisis requires coordinated actions at a global level (Weischer et al., 2012, p. 117; Kulovesi, 2012, p. 193). Attempts to slow down the increase in global temperature, as well as to organize and exchange climate adjustment strategies across jurisdictions, are thus negotiated and coordinated within the framework of the United Nations Framework on Climate Change Convention (UNFCCC). Almost 200 countries are parties to the resulting protocols, and finding consensus on issues such as who needs to make emissions cuts, which sectors and sources to include, has proven challenging (Bodansky and Rajamani, 2013). For instance, Canada, India and until recently also the United States and China have rejected emissions targets established through international negotiations altogether, while less than 40 countries, including the European Union and its member states, are subject to these (Bodansky, 2011). For certain non-committing parties, this may be in line with the principle of common but differentiated responsibilities as incorporated in the Rio Declaration on Environment and Development (see Principle 7 in Rajamani, 2000) but the point in this chapter is not to address the equity of the imbalance in assuming responsibility for climate issues; rather, the focal point here is that this imbalance signifies that only a small percentage of global greenhouse gas emissions are covered by climate protocol norms.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".