Paradoxes and trade-offs in the front-end process of large public projects
Bibliographic record
Abstract
The aim of this conceptual paper is to contribute to a better understanding of the front-end phase of large public projects, which is complex and non-linear. The point of departure relates to a number of paradoxes found along the way of the front-end. A processual approach is taken to follow the front-end over time. Considering a number of example vignettes, four paradoxes and subsequent trade-offs are discussed which affect the strategic decisions that need to be made. These are found to fit largely within four generic sub-processes identified in the front-end. Inspired from the paradox theory, we conceptualise paradoxes and trade-offs under the dynamic equilibrium model adapted for temporary organising such as large public projects. Main aim of this paper is to consider how decision-making can be improved, and managerial strategies developed that permit the acceptance of paradoxes and their resolution in a virtuous cycle leading to long term success.
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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 | 0.025 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".