Curating 1000 flowers as they bloom: Leveraging pluralistic initiatives to diffuse social innovations
Bibliographic record
Abstract
Abstract Research Summary Social and environmental challenges in our society offer opportunities for innovation. Having a strong mission can enhance both opportunity recognition and strategic alignment; however, aligning strategy and mission can be challenging when an organization pursues its social mission in pluralistic ways. How can mission‐driven organizations manage pluralistic local initiatives while cohering to their missions? Using an inductive field study, I trace how Slow Money, an organization fostering sustainable local food systems by connecting food entrepreneurs with local investors, translated its core mission into different mission‐oriented local initiatives. I find that mission‐oriented local initiatives were recombined to create novel strategies curated and diffused by the central leadership, and I show how, rather than derail an organization's mission, pluralistic local initiatives can foster strategies for social innovation. Managerial Summary Organizations addressing social and environmental challenges often are mission driven. Though a mission can help guide strategy decisions, it also can lead to strategy confusion, especially when an organization consists of many local groups with different interpretations of the mission. I use the case of Slow Money, a nonprofit supporting sustainable local food systems, to understand how an organization can transform an assortment of mission‐based strategies into an asset rather than a liability. I find that by promoting an open exchange of local initiatives and strategies, Slow Money's central leadership validated strategy diversity. It also provided its local groups with the opportunity to borrow and repurpose other groups' initiatives. In this way, diverse local strategies created mission unity while also increasing organizational social innovation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".