Enacting responsible leadership in cross-sector partnerships: A dynamic choreography of power
Bibliographic record
Abstract
How can leaders guide diverse organizations to work together on society's biggest challenges when power is unevenly distributed? Cross-sector partnerships (CSPs) bring together companies, governments, and nongovernmental organizations (NGOs) to address complex problems, yet their success hinges on how leaders navigate competing interests and shifting power dynamics. This study examines a high-profile CSP involving a Fortune 500 company, two international development organizations, and an NGO. Drawing on Mary Parker Follett's ideas about "power-with" and "power-over," I show how NGO leaders combined collaborative and more coercive tactics to move the partnership forward. My analysis reveals that responsible leadership (RL) in CSPs is not a static trait or style but evolves through a dynamic choreography of power as challenges and priorities change. The findings offer practical lessons for leaders seeking to balance ethics, inclusion, and influence in multi-stakeholder collaborations, and they extend theory by reframing RL as a shifting, context-sensitive process rather than a fixed style.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.029 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| 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".