FROM FRONTLINES TO BOARDROOMS: LESSONS IN LEADERSHIP AND INNOVATION FROM UNDER THE MANGO TREE
Bibliographic record
Abstract
Abstract The authors (Bartkus, Professor Emerita at the University of Notre Dame; and Block, the George M. Cormie Chair of Management in the Alberta School of Business) write about the applicability of lessons learned in war zones and other extreme environments for today’s business challenges. Dr. Bartkus “founded the Business on the Frontlines Program seeking to harness the dynamism of business in rebuilding societies ravaged by conflict and deep poverty.” Case studies are provided, “from the dusty roads of Uganda to the high‐stakes vaccine distribution efforts of J&J,” during the COVID‐19 pandemic. The authors contend that they “uncover a universal truth about leadership: the most powerful innovations often emerge from the most challenging environments. Whether facing armed middlemen or vaccine skepticism, leaders who can adapt frontline strategies to their unique contexts gain a critical edge in our increasingly complex and interconnected world.” The lessons include, in their words, Map the Entire Landscape and Follow the Money, Build Unconventional Partnerships, Fail fast and forward, and Get Your Boots Dirty. They believe that “effective leaders in challenging environments must look beyond traditional partners and stakeholders. This often means overlooking salient differences and digging deeper to understand the motivations and needs of all parties, including potential adversaries.”
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.020 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".