Can the Liberal Order be Sustained? Nations, Network Effects, and the Erosion of Global Institutions
Bibliographic record
Abstract
A growing retreat from multilateralism is threatening to upend the institutions that underpin the liberal international order. This article applies network theory to this crisis in global governance, arguing that policymakers can strengthen these institutions by leveraging network effect pressures. Network effects arise when networks of actors—say language speakers or users of a social media platform—interact and the value one user derives from the network increases as other users join the network (e.g., the more people who speak your language, the more useful it is because there are more people with whom you can communicate). Crucially, network effect pressures produce what is called ‘lock-in’—a situation in which actors are unable to exit the network without incurring high costs and as a result become locked into the network. For example, because of their powerful network effect pressures, users of Facebook and the English language cannot easily exit these networks. International organizations such as the UN, the WTO, the IMF, etc., are networks of sovereign states that likewise produce network effect pressures. As such, intensifying their network effect pressure can lock countries more firmly into these institutions. To that end, this article proposes a suite of strategies policymakers may use to manipulate the network effect pressures generated by international organizations to strengthen these institutions and the multilateral treaties that establish them—an approach the article calls treaty hacking. The article offers a toolkit from which policymakers can draw to bolster the liberal order in the face of growing global instability and change.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.050 |
| Scholarly communication | 0.021 | 0.025 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 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".