Policy-driven projects: Empowering the world to confront grand challenges
Bibliographic record
Abstract
The world is beset by grand challenges, those messy, multi-stakeholder, value-laden, complex, wicked, and constantly evolving problems like climate change, unsustainable development, economic inequality, and global pandemics. While these grand challenges call for projects to translate policies into actions and changes, these projects tend to fall short of their ‘beautiful’ goals- such as the United Nations' Sustainable Development Goals (SDGs). Yet, the management literature remains mostly silent about the role of projects and project delivery in tackling grand challenges. In this article, we discuss the contribution of projects to grand challenges and ask what type of project management they demand, particularly in Europe. In light of the sheer complexity and interdependency of grand challenges and the projects they provoke, we call for further collaboration between project scholars and management scholars to help policymakers shape a better world where no one is left behind.
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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 | 0.024 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.002 | 0.024 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".