The Renewable Energy Commons: Global Public Goods, Governance Risk, and International Energy
Bibliographic record
Abstract
"For years, the great bane of international cooperation has been the much-scorned free rider. International public goods such as climate change mitigation, vaccination against disease, reduction in acid rain, and preservation of the ozone layer all require incentivizing states to participate in international institutions when the individually rational thing to do is remain on the sidelines. Lawyers, policymakers, and scholars have come up with a host of devices to deter free riding and encourage participation in global public goods. Issue linkages, trade sanctions, financial assistance, and minimum participation requirements are just some of the carrots and sticks that states use in international public goods institutions. And these efforts have frequently been successful. For example, the Montreal Protocol, which governs ozone-depleting substances and uses financial assistance for developing countries as a carrot coupled with the stick of trade sanctions against non-members, has near-universal membership and has been haled as the single most successful environmental agreement to date. But as the end of 2012 draws near, the inability to conclude a successor agreement to the Kyoto Protocol is forcing commentators to rethink their approach to supplying global public goods. The traditional tools of international governance have proven inadequate to generate meaningful international cooperation on climate change mitigation. What, then, is the way forward?"
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".