Megaprojects
Bibliographic record
Abstract
“Megaprojects” is a term used to refer to projects and events that encompass large-scale projects in size, cost, space, time, energy, and influence. They are synonymous with large engineering projects, complex projects, large transport or energy projects, and large infrastructure projects, and are often composed of multilayered discrete projects forming a larger scale complex project. Some of the complexity deals with difficulty in quantifying the long-terms costs or benefits or fully realising the whole life cycle of the megaproject prior to commencement. Megaprojects are often shaped by contextual factors. Where complexity is related to technical aspects of the project it also includes organizational aspects and the scope of the project. Some of these projects are multifaceted and relate to science research, engineering infrastructure, or private and public construction of buildings and/or other venues. Megaprojects affect societies that undertake them, urban planning aspects, and social relationships between stakeholders engaged in executing all the elements involved in creating them. They have an impact on a number of areas both locally and globally. This includes extending notions of urban planning to accommodate large-scale construction. These projects can be significant in terms of social and/or economic factors in a positive or negative sense. There have been debates and criticism on the need and function of megaprojects and whether they are beneficial constructs or detrimental to society.
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.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.268 | 0.084 |
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".