<i>De jure</i> and <i>de facto</i> inclusivity in global governance
Bibliographic record
Abstract
Abstract Global governance institutions have increasingly ‘opened up’ to non-state actors, leading to more formally inclusive governance arrangements. This has prompted inquiry into the extent and the drivers of this inclusivity, patterns of participation, and the consequences for the legitimacy and effectiveness of global governance. However, while the measurement of formal openness has expanded, the quality of inclusion remains underexplored. We therefore introduce a framework centred on the notion of ‘meaningful inclusion’, distinguishing between formal ( de jure ) structures and the perceived quality of actual ( de facto ) engagement. Drawing on extensive empirical data, we then examine the Global Partnership for Effective Development Cooperation. This case exemplifies strong formal mechanisms for inclusion that are contrasted sharply by significant shortcomings in effective engagement. Our findings suggest that improvements in formal global governance structures alone cannot ensure meaningful inclusion. Instead, we highlight the centrality of power dynamics and vested interests in shaping inclusivity dynamics in practice.
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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 | 0.017 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.046 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".