Leading the unwilling: Unilateral strategies to prevent arctic oil exploration
Bibliographic record
Abstract
Arctic oil extraction is inconsistent with the 2 °C target. We study unilateral strategies by climate-concerned Arctic countries to deter extraction by others. Contradicting common theoretical assumptions about climate-change mitigation, our setting is one where countries may fundamentally disagree about whether mitigation by others is beneficial. This is because Arctic oil extraction requires specific R&D, hence entry by one country expands the extraction-technology market, decreasing costs for others. This means that, on the one hand, countries that extract Arctic oil gain if others do so as well. On the other hand, as countries may disagree about how harmful climate change is, they may disagree whether an equilibrium where all enter is better or worse than an equilibrium where all stay out. Less environmentally-concerned countries (preferring maximum entry) have a first-mover advantage but, because they rely on entry by others, entry in equilibrium is determined by the preferences of those who are moderately concerned about the environment. Furthermore, using a pooling strategy, an environmentally-concerned country can deter entry by credibly “pretending” to be environmentally adamant, and thus be expected to not follow. A rough calibration suggests a country like Norway, or prospects of a green future U.S. administration, could be pivotal in determining whether the Arctic will be explored.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".