Bibliographic record
Abstract
Abstract Why are some institutions without any policy powers or output? This study documents the efforts by governments to create empty international institutions whose mandates deprive them of any capacity for policy formulation or implementation. Examples include the United Nations Forum on Forests, the Copenhagen Accord on Climate Change, and the UN Commission on Sustainable Development. Research is based on participation in twenty-one rounds of negotiations over ten years and interviews with diplomats, policymakers, and observers. The article introduces the concept of empty institutions, provides evidence from three empirical cases, theorizes their political functions, and discusses theoretical implications and policy ramifications. Empty institutions are deliberately designed not to deliver and serve two purposes. First, they are political tools for hiding failure at negotiations, by creating a public impression of policy progress. Second, empty institutions are “decoys” that distract public scrutiny and legitimize collective inaction, by filling the institutional space in a given issue area and by neutralizing pressures for genuine policy. Contrary to conventional academic wisdom, institutions can be raised as obstacles that preempt governance rather than facilitate it.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.054 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".