Partners for Good: How Business and NGOs Engage the Commercial–Social Paradox
Bibliographic record
Abstract
Businesses and NGOs are collaborating more frequently to address social issues with commercial solutions, yet not all collaborations work well. We wanted to know why some collaborations struggle where others succeed. We studied five projects in India in which businesses bought goods and services from NGOs that employed disadvantaged people. Two of these five projects met the expectations of both parties, whereas the other three did not. By drawing on the paradox literature, we argue that the project’s success indicates that the business and NGO engaged the commercial-social paradox. We found that in the projects that worked well, the two parties held fluid categories, i.e. they saw differences between business and NGO as contextual and aimed to find creative workarounds to emergent problems. In the projects that did not work well, businesses and NGOs imposed categorical imperatives, i.e. they saw sharp differences that they intensified by imposing standardized and familiar solutions on their partner. We contribute to the literature on paradox to show how cognition and action create generative or limited outcomes. We also weigh in on the ontological foundations of paradox, arguing that actors that assume that paradoxes are a social construction are more likely to engage paradoxes than actors that assume paradoxes are a social reality.
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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 | 0.025 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.019 | 0.035 |
| Scholarly communication | 0.023 | 0.028 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".