Climbing to the top: Personal life stories on becoming megaproject leaders
Bibliographic record
Abstract
This paper captures a better understanding of the career development of people leading megaprojects through the use of biographical research method. The characteristics of megaprojects cause serious and diverse challenges for their leaders, but programs where they are trained to overcome these challenges are not easily available around the world. We used a biographic research to gather sixteen life histories of megaproject leaders from ten different countries. This approach helps to explore megaproject leaders as people and how they have learned to become leaders. Findings show that leaders learned to manage megaprojects through a lifetime interaction of: (1) personal characteristics of leaders, (2) turning points in their lives, (3) value orientations stemming from their family, region or religion, (4) their relationship to the project team, and (5) their professionalization through a diversity of projects. These findings add to our knowledge on leaders’ career development that this not only depends on individual agency but also on contextual influences which span a lifetime. Furthermore, the findings contribute to the debate on narrative inquiry methods by demonstrating the full potential of biographical research method for understanding megaproject leadership. Finally, the findings contribute to the debate on megaprojects leaders with real accounts of how people have become leaders through self-development.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".