How do grand challenges determine, drive and influence the innovation efforts of for‐profit firms? A multidimensional analysis
Bibliographic record
Abstract
Abstract While raising concerns, the recent proliferation of grand challenges has sparked interest in the role played by innovation in causing them, and in how the attempts made to fix them may cause even greater challenges that present themselves down the line. This article provides an analysis of the bibliographic metadata, published between 2002 and 2020, focusing explicitly on the private‐for‐profit sector. By identifying common themes from 66 documents, a framework highlighting the shared concerns and research trajectories was derived. Our results are illustrated and discussed along 11 research themes. We contribute theoretically by identifying the innovation efforts of for‐profit firms that directly relate to grand challenges, through two cases of carbon capture and storage and deep‐sea mining. We conclude that a more holistic understanding of innovation and its many possible consequences needs to be developed. We highlight the limitations of perspectives that do not always take full account of the potential divergence of interests between stakeholders, and, how fuller input by a greater cross‐section of stakeholders may help identify any negative effects of innovations at an earlier stage. Informed by recent extensions of social innovation theory, we explore the potential for synthesis around a pragmatic understanding of institutions, stakeholders, and the nature and quality of ties that bind 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 | 0.016 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".