Grand Challenges Viewed through the Pragmatist Lens of the Economies of Worth: A Multidisciplinary Review and Framework for the Conduct of Moral Work in Pluralistic Settings
Bibliographic record
Abstract
Abstract A fast‐growing number of organization and management scholars are responding to calls to conduct research on grand challenges (GCs). Few among these, however, question the core assumptions that underpin their efforts. In this paper we argue that the intractability of GCs stems from a failure to recognize the fundamentally pragmatic, plural, and moral character of these problems, which generate conflicts between groups over what is the ‘right’ or most appropriate course of action to pursue. A theoretical lens frequently used across many disciplines to make sense of problems such as these is Boltanski and Thévenot's (1991, 2006) economies of worth (EoW). On this premise, we undertake a multidisciplinary review of articles that use the EoW for studying GCs. Based on our analysis, we develop a pragmatist framework that articulates the practices that underpin the conduct of ‘moral work’ that organizational actors engage in as they seek to agree on a common sense of justice in GC contexts. Our framework provides a useful roadmap for scholars interested in applying a pragmatist perspective to our understanding of GCs, and by so doing, explore different, more socially just, and potentially more impactful ways of tackling them.
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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 | 0.022 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.021 | 0.019 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".