Generativity as a heuristic for impact-driven scholars addressing grand challenges
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it