Grand Challenges Unpacked: Strategic Implications of Coordination, Competition, and Communication
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 an era of significant societal challenges like climate change and biodiversity loss, how do we best tackle these complex issues? This symposium presents a reevaluation of traditional methods, spotlighting nuanced strategies in the realm of grand challenges (GCs). Addressing gaps in current GC literature, our symposium uniquely balances centralized, public coordination with decentralized, market-driven competition. Our set of five papers explore the microfoundations of societal problem-solving, traditionally depicted as a monolithic goal yet demanding systematic unpacking. Our inquiry of organizational modes spans from the roles of mission-oriented R&D programs, public-private partnerships to innovation tournaments, in the face of environmental and regulatory uncertainties. Our papers also employ diverse cutting-edge methods, including abductive reasoning, machine learning, and content analysis. This approach encourages the exploration of novel solutions to complex problems, particularly valuable in uncertain scenarios where not all variables are observable, and creative, interdisciplinary thinking is required. We examine a variety of high-risk, high-tech industries such as radar, autonomous vehicles, space exploration, carbon capture, and Covid testing. Challenging the prevailing emphasis on macro-level coordination, we unveil the transformative impact of competition and laissez-faire approaches. These studies collectively investigate how nuanced communication, regulatory dynamics, and collaborative efforts can reshape our understanding of GCs, offering innovative solutions and strategic insights. Join us to delve into the multifaceted world of GCs, where innovative approaches redefine societal problem-solving. This symposium is an invitation to rethink, rediscover, and respond to the most pressing challenges of our time with fresh perspectives and groundbreaking research methods. Coordinated R&D Programs and the Creation of New Industries Author: Daniel Gross; Fuqua School of Business, Duke U. Author: Maria Roche; Harvard Business School Identification of the solution to an exploratory search to address a grand challenge: NASA’s search Author: Raja Roy; New Jersey Institute of Technology Author: Francisco Polidoro; U. of Texas at Austin Author: Curba Morris Lampert; Florida International U. Author: Minyoung Kim; The Ohio State U. Scaling up to drawdown: entrepreneurs and system architects in the nascent carbon removal industry Author: Angie Otteson Fairchild; Kenan-Flagler Business School, U. of North Carolina at Chapel Hill Author: Timothy Ott; U. of North Carolina, Chapel Hill Author: Mahka Moeen; U. of Wisconsin Who will go against the will of the regulator? A study of the nascent COVID-19 test industry Author: Seojin Kim; Drexel U. Author: J.P. Eggers; New York U. The DARPA Grand Challenge and the Emergence of the Autonomous Vehicle Ecosystem Author: Eunhee Sohn; Georgia Institute of Technology Author: Vijayaraghavan Venkataraman; Indian Institute of Management, Bangalore Author: Hye Young Kang; Ewha Womans U.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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