Cross‐Sector Partnership Research at Theoretical Interstices: Integrating and Advancing Theory across Phases
Bibliographic record
Abstract
Abstract Cross‐sector partnerships (XSPs) are embraced by policymakers and practitioners to address complex social and environmental challenges that no single sector can tackle alone. However, extant research on XSPs has primarily focused on isolated phases and singular theoretical perspectives. In our paper, we synthesize XSP research in the public policy and management fields to deliver a comprehensive and coherent understanding of XSPs’ different phases and theoretical perspectives – the XSP ‘theoretical topology’. We introduce two approaches for theoretical enrichment: informing and interacting. We emphasize the significance of ‘theoretical interstices’ as undominated spaces for new knowledge exploration. Through our integrative cross‐phase, cross‐theoretical approach, we address fundamental yet open questions on XSP effectiveness, value, and impact. Our work challenges existing understandings and opens new research possibilities; offers implications for practitioners; and informs current policy debates on mandating XSPs and on the role of ‘big data’ – powered algorithms in the XSP landscape.
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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 | 0.067 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.006 | 0.048 |
| Scholarly communication | 0.021 | 0.043 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".