Tackling Grand Challenges Pragmatically: Robust Action Revisited
Bibliographic record
Abstract
In this article, we theorize a novel approach to addressing the world’s grand challenges based on the philosophical tradition of American pragmatism and the sociological concept of robust action. Grounded in prior empirical organizational research, we identify three robust strategies that organizations can employ in tackling issues such as climate change and poverty alleviation: participatory architecture, multivocal 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. We conclude by discussing our contributions to research on robust action and grand challenges, as well as some implications for research on stakeholder theory, institutional theory and theories of valuation.
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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 | 0.045 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.093 |
| Scholarly communication | 0.015 | 0.031 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.010 | 0.010 |
| 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".