From market multilateralism to governance by goal setting: SDGs and the changing role of partnerships in a new global order
Bibliographic record
Abstract
Abstract Business has been involved in cooperation with multilateral organizations through public-private partnerships (PPPs) since the late 1990s. With their adoption of the sustainable development goals (SDGs), multilateral institutions increasingly consider partnerships as a means to achieve their goals given their own limited implementation capacity. However, the global economic order has changed significantly since the first expansion of PPPs, particularly due to growing participation by non-western states and companies. This article asks how this shift has changed the eagerness to form partnerships, as well as their qualitative content. It analyzes the 3964 partnerships in the SDG partnership registry, focusing on the subset of them that includes business partners. We divide these into five groups: local implementation, resource mobilization, advocacy, policy, and operational partnerships. We study PPPs involving companies from different varieties of capitalism—private, market based forms, and state-led forms of capitalism. We find that PPPs are still dominated by companies and other actors from Western countries. Moreover, business participate more in U.S.- and Canadian-led partnerships than others. We also find strong differences regarding what category of PPPs that companies from different backgrounds engage in, and discuss the linkages between varieties of capitalism and PPP participation.
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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 | 0.021 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.030 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| 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".