Standing on the Shoulders of Giants: Leveraging Management Research on Grand Challenges
Bibliographic record
Abstract
We advance research on how businesses engage with the complex social problems currently known as Grand Challenges. We study the concepts that preceded the term Grand Challenges, the connected ontologies that ground them, and the diversity of perspectives they offered. We construct a knowledge map that includes well-researched obstacles, such as governance obstacles hindering engagement and sensemaking obstacles limiting the ideation of novel and creative efforts. But we also build on prior research to identify curation obstacles, which precede engagement and define which problems receive social attention, and adaptation obstacles, which create uncertainty over workable solutions and bias the momentum of social systems toward the status quo. Our broader view on the obstacles defining Grand Challenges opens new pathways and identifies underexplored levers by which to understand and influence business engagement with complex social problems.
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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 | 0.027 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.013 | 0.038 |
| Scholarly communication | 0.033 | 0.048 |
| Open science | 0.003 | 0.027 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".