Cross-Sector Collaboration: Mapping the Experience Levels of Development Organizations
Bibliographic record
Abstract
This qualitative study maps international development organizations based on their respective levels of experience in cross-sector collaboration. The trend of cross-sector collaboration is taking increasing importance for nonprofit organizations. Donors consider collaboration between nonprofits and businesses as a key to achieving development goals. Based on surveys and in-depth interviews with leaders and senior managers of international development organizations from around the world, we identify three distinct clusters in terms of experience levels in cross-sector collaboration: the Beginners, the Intermediates, and the Advanced. We describe and analyze each of these three clusters with regard to their specific contexts, dynamics and objectives. While this trend has been researched from the perspective of businesses, less is known on the perspective of NGOs regarding cross-sector collaborations and partnerships. Our article contributes to the academic literature by illustrating how the stages of cross-sector collaboration unfold in practical terms as a gradual learning and experience-building process. It also helps practitioners conceptualize, plan and structure their efforts towards the next steps in their progression along the cross-sector collaboration continuum.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| 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".