Robust Action Strategies for Tackling the World's Grand Challenges
Bibliographic record
Abstract
Organizations are increasingly interested in contributing to so-called grand challenges such as climate change and poverty alleviation. In this chapter, excerpted from Tackling Grand Challenges Pragmatically: Robust Action Revisited (Ferraro et al., 2015), we summarize a novel approach to addressing the world's grand challenges based on the sociological concept of robust action. Grounded in prior empirical organizational research, we identify three robust strategies that organizations can employ: participatory architecture, multi-vocal inscriptions, and distributed experimentation. We demonstrate how these strategies operate, the manner in which they are linked, the outcomes they generate, and why they are applicable for resolving grand challenges. For those readers interested in a fuller exposition, we suggest consulting the original article (Ferraro et al., 2015) as well as a companion article that examined the relevance of robust action strategies in three sustainability related contexts and compared our theory of robust action strategies with alternative management approaches (Etzion et al., 2017).
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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 | 0.029 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.050 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".