The Cultural Ecosystem of Megaprojects: The Interconnectedness of Organizational Elements and their Wider Institutional Contexts
Bibliographic record
Abstract
In the multi-layered structure of megaprojects, organizational cultures emerge within different temporary multi-organizational teams (TMOs) responsible for execution.The cultural ecosystem in megaproject is conceptually articulated at the levels of their institutional settings to increase sustainable delivery.TMOs as meso-level coalitions are complex systems comprised of diverse public-private organizations responsible for delivering individual projects within the programme.Understanding these social and structural milieus provides a new groundwork for conceptual development and theorization of megaproject culture formation.Mirrored through its dynamics, ecology and development, this paper unravels how cultural features are affected by environment, effected in development, and transformed during the megaproject life.Contributions are made by shifting discourse towards a dynamic understanding of causal diversity that shape culture within TMOs to improve transferable practice.This investigative strategy challenges extant normative viewpoints.Indicating the need to reconsider existing perceptions and to embed norms of reflexivity beyond the mainstream megaproject management thinking.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".