Mitigating Inequalities of Influence Among States in Global Decision Making
Bibliographic record
Abstract
International institutions should be as equal as they claim to be, especially since many of them assert superordinate normative authority based on having egalitarian governance structures. However, when defining equality with respect to states’ real-world influence in determining substantive outcomes, it is evidence that there is an equality-influence gap between the rhetoric of parity among states and the reality of international politics. This is problematic because it undermines trust in those international institutions that falsely claim to embody equality among states when empirically they do not. Focusing on the United Nations System, this paper identifies three main causes of this disproportional influence among states in global decision making: (a) external imbalances in political capital; (b) internal economic barriers; and (c) surreptitious influence through non-state actors, funding and training. Six pragmatic strategies are proposed for mitigating these inequalities: (1) building capacity for leadership in global advocacy; (2) supporting global networks owned by developing countries; (3) equalizing multi-party partnerships; (4) facilitating evidence-informed global decision making; (5) enhancing accountability and independent evaluation; and (6) encouraging further discussion on institutional reforms. Notwithstanding sovereign equality’s deep flaws, it is hoped that challenging the egalitarian presumptions of global decision making will encourage further debate on this issue among those who can act upon it.
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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 | 0.028 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.022 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".