International Institutions, Global “Partnerships” and the Structural Power of Multinational Corporations
Bibliographic record
Abstract
Throughout the last two decades, institutions of global security and governance have undergone a paradigmatic shift in their engagements with multinational corporations (MNCs). The United Nations, in particular, has increasingly embraced big business as “partner” in human security, humanitarian response and development through formalized “global public–private partnerships” (GP3s). Naturally, a debate has emerged on the efficacies of these GP3s and their implications for global governance. This paper contributes to this debate by proposing and employing a new research agenda that interrogates the impacts that GP3s have on international institutions themselves using a case study of a particular UN agency, the United Nations High Commissioner for Refugees (UNHCR). It will argue that UNHCR GP3s are a highly asymmetrical set of power relations that are having constitutive effects on the agency. The UNHCR is undergoing significant operational and ideological changes in the GP3 process in a manner that is synonymous with Stephen Gill’s (1998) concept of “new constitutionalism”; a reconstitution that opens up and further embeds the agency within the forces of the capitalist global political economy. Hence, this case study demonstrates that GP3s are capable of undermining the mandate and autonomy of global security and governance institutions.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.035 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.010 |
| 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".