Bibliographic record
Abstract
The United Nations’ Sustainable Development Goal (SDG) 17 encourages global partnerships for sustainable development, of which cross-sector partnerships (CSPs) are a particularly important mechanism. Yet, a good amount of evidence is emerging that CSPs fail to match their objectives and often fall into inactivity. In this paper, we argue for a new conceptualization of why partners form CSPs that illuminates these common failings and suggests ways to overcome them. We put the question of the partners’ perceived social risks at the center of CSP formation and suggest CSPs emerge primarily as a social risk mitigation practice. Using semi-structured interviews to explore how partners understand social risk, we catalogue how multi-national corporations (MNCs), governments, non-governmental organizations (NGOs) and others perceive the likelihood and consequences of social risks. Our analysis, grounded in convention theory (CT), identifies six perceived social risks and three overarching mitigation practices. Our contribution is broadly to the CSP literature as well as to an emerging stream that focuses on social risk.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| 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".