Generativity as a heuristic for impact-driven scholars addressing grand challenges
Bibliographic record
Abstract
In this contribution, we theorize generativity as a heuristic for impact-driven management scholars seeking to address grand challenges through research. We use generativity to connote the engagement of diverse actors in pluralistic inquiry to create conditions for future flourishing. Our theorization applies a pragmatist worldview and builds on insights from the multidisciplinary literature on generativity to envisage researchers as agents of care, collective learning, and transformative change. We synthesize four tenets for researchers seeking both academic and real-world impact. These tenets can support researchers addressing grand challenges by guiding their efforts to diversify inputs, distribute agency, conduct experiments, and pursue prospective impacts. We illustrate generativity in action by drawing on our experience in a transdisciplinary research project on small- and medium-sized enterprises taking climate action in Canada. We show how the four tenets foster generativity to promote an inclusive understanding of grand challenges and a bias toward action, thereby providing an optimistic stance toward addressing issues of social concern.
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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 | 0.035 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.010 | 0.079 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.005 | 0.006 |
| 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".