Domesticating the Beast: A “Resource Profile” Framework of Power Relations in Nonprofit–Business Collaboration
Bibliographic record
Abstract
Researchers have addressed the implications of power imbalance for nonprofits engaging in collaborations with businesses. Yet as nonprofit–business collaboration intensifies, nonprofit managers’ perceptions of power asymmetry in these relationships remain scantly studied. We argue that investigating these perceptions can sharpen the understanding of determinants and processes of power relations from a nonprofit perspective. To do so, we studied nonprofit–business collaboration in a network of international cooperation nongovernmental organizations (NGOs). Based on our findings, we designed a nonprofit-centric “resource profile” framework of power relations in cross-sector collaborations. This framework provides an empirically grounded tool to inform nonprofit managers’ decision making as they engage in collaborations with businesses. Based on this framework, we elaborate a set of theoretical propositions to integrate existing knowledge and guide further nonprofit-centric research on power dynamics in cross-sector collaboration.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".