Academics Beyond Academia: Management Scholars on the Ground to Address Grand Challenges
Bibliographic record
Abstract
Recent research in management emphasizes the need to engage with grand challenges of our time, such as climate change, inequality, poverty, ecological imbalances, conflict, socioeconomic and political crises, etc. in which organizations, their managers, and other stakeholders are implicated and by which they are also impacted in various ways. We take this conversation further and in a new direction through a panel of academics who have gone ‘beyond academia’. In addition to publishing research related to such challenges, these academics have also engaged in significant efforts ‘on the ground’ to address such challenges through policy work, social activism, leadership roles outside of typical academic settings, roles in social movements, becoming part of solution implementation in communities, and so on. Our symposium discusses how the exploration of such roles by management academics can open up our field to make an impact towards addressing grand challenges in direct ways and also feed back into theory, methodology, and pedagogy in unexpected and fortuitous ways.
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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 | 0.064 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.053 | 0.071 |
| Scholarly communication | 0.060 | 0.067 |
| Open science | 0.007 | 0.054 |
| Research integrity | 0.028 | 0.043 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".