Appendix B: Attendees at the IMEC Forums in Montreal, Poitiers, and James Bay
Bibliographic record
Abstract
As the number, complexity, and scope of large engineering projects (LEPs) increase worldwide, the huge stakes may endanger the survival of corporations and threaten the stability of countries that approach these projects unprepared. According to the authors, the front-end engineering of institutional arrangements and strategic systems is a far greater determinant of an LEP's success than are the more tangible aspects of project engineering and management.The book is based on an international research project that analyzed sixty LEPs, among them the Boston Harbor cleanup; the first phase of subway construction in Ankara, Turkey; a hydro dam on the Caroni River in Venezuela; and the construction of offshore oil platforms west of Flor, Norway. The authors use the research results to develop an experience-based theoretical framework that will allow managers to understand and respond to the complexity and uncertainty inherent in all LEPs. In addition to managers and scholars of large-scale projects, the book will be of interest to those studying the relationship between institutions and strategy, risk management, and corporate governance in general.Contributors Bjorn Andersen, Richard Brealey, Ian Cooper, Serghei Floricel, Michel Habib, Brian Hobbs, Donald R. Lessard, Pascale Michaud, Roger Miller, Xavier Olleros.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".