Bias versus error: why projects fall short
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose Worldwide, major projects often make the headlines as they suffer from a fourfold whammy of delays, cost blowouts, benefit shortfalls and stakeholder disappointments. It seems that error and bias can explain their underperformance. Which overarching explanation outweighs the other? It is the question this paper aims to address. Design/methodology/approach Insights are garnered from decades of research on thousands of major projects in developed and developing countries worldwide. In particular, two high-profile project cases, the Veteran Affairs Hospital in Aurora, Colorado (USA) and the Philharmonie de Paris (France), are explored. Findings The case projects show that error and bias combine to best explain project (under) performance. Applying best practices or debiasing project cost and benefit estimates is insufficient to prevent cost blowouts and benefit shortfalls. The confrontation of the two overarching explanations is not merely platonic. It is real and may lead to a media and legal battle. Originality/value This viewpoint calls practitioners to transcend the error versus bias debate and reconcile two key characters in the world of major projects: the “overoptimistic” who hold a bias for hope and firmly believe that, despite error down the road, many projects would, in the end, “stumble into success” as creativity may come to the rescue; and the “overpessimistic” who hold a bias for despair and think many projects should not have been started.
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.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 it