Understanding conditions for cooperation when regulating common resources: Why is climate change so difficult to govern?
Bibliographic record
Abstract
International community has had little success in solving the problem of governing shared natural resources, of which the changing climate is the most prominent example. I wanted to know why. To answer this question I explored the conditions for achieving collective goals and providing for public goods, using the theory of collective action as a tool for analysis. First I examined collective action conditions in local settings, by looking into research conducted on local level natural resource management. Based on insights from the local settings I further investigated conditions for collective action in the international system. That I did by presenting the fundamental differences between the Montreal Protocol and the Kyoto Protocol, setting out an overview of existing international cooperation on climate change. Due to the anarchical nature of the international system with no central authority to enforce laws, agreements between states cannot be legally binding, which gives the states strong incentive to free-ride. In a system with economy based on competition where states are primarily interested in growth, the prospects for successful joint efforts to curb dangerous climate change seem rather gloomy.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".