Cross‐Sector Partnerships to Address Societal Grand Challenges: Systematizing Differences in Scholarly Analysis
Bibliographic record
Abstract
Abstract Research on how cross‐sector partnerships (CSPs) contribute toward addressing societal grand challenges (SGCs) has burgeoned, yet studies differ significantly in what scholars analyze and how. These differences matter as they influence the reported results. In the absence of a comprehensive framework to expose the analytical choices behind each study and their implications, this diversity challenges interpretation and consolidation of evidence upon which novel theory and practical interventions can be developed. In this study, we conduct a systematic review of scholarly analysis in CSP management studies to develop a framework that contextualizes the SGC‐related evidence and reveals scholars’ analytical choices and their implications. Conceptually, we advance the term ‘SGC interventions’ to illuminate the black box leading to SGC‐related effects, thus helping to differentiate between transformative versus mitigative interventions in scholars’ analytical focus. Moreover, the framework stresses the logical interplay between the framing of the SGC‐related problem and the reporting of the intervention's effects. Through this, we juxtapose what we call problem‐centric versus solution‐centric SGC analysis and so differentiate between their analytical purpose. We discuss the framework's implications for advancing an SGC perspective in scholarly analysis of CSPs and outline avenues for future research.
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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 | 0.308 | 0.417 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.027 | 0.024 |
| Science and technology studies | 0.007 | 0.036 |
| Scholarly communication | 0.024 | 0.035 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.003 | 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".