Cross‐Sector Partnerships to Address Societal Grand Challenges: Systematizing Differences in Scholarly Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it