Front end Governance of Major Public Projects - Lessons with a Norwegian Quality Assurance Scheme
Bibliographic record
Abstract
Governance regimes for major investment projects comprise the processes and systems that need to be in place on behalf of the financing party to ensure successful investments. This would typically include a regulatory framework to ensure adequate quality at entry, compliance with agreed objectives, management and resolution of issues that may arise during the project, etc., and standards for quality review of key governance documents. The challenges are abundant: How to ensure projects’ viability and relevance up-front; how to avoid hidden agendas during planning, underestimation of costs and overestimation of utility, unrealistic and inconsistent assumptions; how to secure essential planning data, adequate contract regimes, etc. \nThis paper discusses measures in terms of governance regimes that might improve success in public investment projects. Success is defined at two levels; 1) operational (efficiency and cost control), and 2) strategic (effectiveness and viability) . As a special case, we present the Norwegian project governance regime applicable to major public projects, which has existed since year 2000. It comprises two quality assurance exercises in the front-end phase, aimed to ensure an adequate basis for the political go/no go decisions, but with no involvement during project implementation. The experience we have so far is positive and shows that the regime most likely leads to more successful projects at both levels.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".