The Quest for Hegemony Among Countries and Global Pollution
Bibliographic record
Abstract
This paper builds on the assumption that countries behave in such a way as to improve, via their economic strength, the probability that they will attain the hegemonic position on the world stage. The quest for hegemony is modeled as a game, with countries being differentiated initially only by some endowment which yields a pollution free ow of income. A country's level of pollution is assumed directly related to its economic strength, as measured by its level of production. Two types of countries are distinguished: richly-endowed countries, for whom the return on their endowment is greater than the return they can expect from winning the hegemony race, and poorly-endowed countries, who can expect a greater return from winning the race than from their endowment. We show that in a symmetric world of poorly-endowed countries the equilibrium level of emissions is larger than in a symmetric world of richly-endowed countries: the former, being less well endowed to begin with, try harder to win the race. In the asymmetric world composed of both types of countries, the poorly-endowed countries will be polluting more than the richly endowed countries. Numerical simulations show that if the number of richly-endowed countries is increased keeping the total number of countries constant, the equilibrium level of global emissions will decrease; if the lot of the poorly-endowed countries is increased by increasing their initial endowment keeping that of the richly-endowed countries constant, global pollution will decrease; increasing the endowments of each type of countries in the same proportion, and hence increasing the average endowment in that proportion, will decrease global pollution; redistributing from the richly-endowed in favor of the poorly-endowed while keeping the average endowment constant will in general result in an increase in the equilibrium level of global pollution.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".