Paradoxes and trade-offs in the front-end process of large public projects
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
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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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it