Guidelines for the Deployment of Criteria for Selection of Strategic Projects in Design
Bibliographic record
Abstract
Strategic projects are selected by executives based on pre-established criteria. These criteria must be deployed for product development so that they can be present as attributes in the development process. In an industrial context, this study identifies criteria that are relevant to executives and meet their expectations about convergence with the strategic vision of their organization, and it sheds light on how these criteria are unfolded to achieve the development of products. A literature review produced a theoretical background about the definition of project selection criteria, and interviews were conducted with executives from different companies for practical reference regarding how the selection process actually happens in organizations. Finally, study groups were formed to discuss the means by which the criteria migrate from managers' expectations to actual products in the development process. As a result, a set of guidelines was compiled so that companies can deploy their criteria to select strategic projects in project attributes and operational actions throughout the product development process.
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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 | 0.130 | 0.208 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.021 | 0.016 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".