Impacting Grand Challenges: A 'Both/And' Approach
Bibliographic record
Abstract
In this panel symposium, we seek to build on growing efforts by management scholars to engage with grand challenges and the United Nations’ Sustainable Development Goals (SDGs). Reflective of the All- Academy Theme description, we note that research and scholarship around addressing these important issues traditionally adopts an either/or approach, reflecting a false self-imposed dichotomy that prevents more expansive and synergistic thinking. To advance scholarship and insights about the management of grand challenges, we turn to paradox theory, an organizational lens gaining attention among organizational scholars that explores the nature of competing demands and unpacks approaches to move beyond either/or thinking into more both/and approaches. We structured this session to be an interactive panel discussion with scholars who have deep and rich knowledge of specific SDGs. They will both share their knowledge and engage in a robust and provocative discussion to address important questions about the role of both/and thinking to address grand challenges, and how management scholars can advance that work. We hope that bringing a paradox lens to these issues will deepen our scholarship, as well as help push forward on practices in academia that allow us to be more relevant and impactful in building a thriving and sustainable world.
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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 | 0.036 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.034 |
| Scholarly communication | 0.027 | 0.028 |
| Open science | 0.006 | 0.029 |
| Research integrity | 0.014 | 0.023 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".