Addressing Grand Challenges Through Unconventional Leadership Styles and Novel Organizing Mechanisms
Bibliographic record
Abstract
Grand challenges, such as global warming and social inequality, are evaluative, complex and uncertain global problems that manifest locally and operate across different scales and geographical locations. Addressing these challenges requires deep knowledge of affected communities and the broader value systems that shape them. Over time, research has focused on leadership styles, intra- and interorganizational mechanisms that enable actors to deal with the multidimensionality proper of grand societal challenges. However, more research is needed on how leaders adapt their leadership style over time to mobilize support among stakeholders to address grand challenges, how they change their perception of themselves and the challenges they are addressing. Furthermore, we need a deeper understanding of how organizations can deal with actors operating at different scales and locations as well as of the conditions that favor the implementation of innovative solutions to address grand challenges. The objective of this symposium consists in addressing these issues and in providing a comprehensive view of the different challenges and opportunities that individuals and organizations face when providing innovative solutions to grand challenges. We bring together four papers, that collectively contribute to a multilevel understanding of the mechanisms and processes that can be performed to address grand challenges. Dynamic tensions of counter-normative leadership: An identity work perspective Author: Marya Besharov; Oxford U., Saïd Business School Author: Susanna Kislenko; Oxford U., Saïd Business School Author: Tracy A Thompson; U. of Washington, Tacoma Author: Gervase R. Bushe; Beedie School of Business Simon Fraser U. How structural and programmatic scaffolds enable knowledge transfer in international development Author: Rodrigo Canales; Boston U. Author: Mikaela Bradbury; Goldman Sachs Author: Anthony Sheldon; Yale School of Management How to interweave clock-time and event-time in open social innovation Author: Anne-Laure Fayard; NOVA School of Business and Economics Double goal double trouble? Orchestration mechanisms to address intertwined grand challenges Author: Silvia Velmer; IESE Business School Author: Tommaso Ramus; ESSEC Business School Author: Antonino Vaccaro; IESE Business School Author: Stefano Brusoni; ETH Zürich
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".