Planning Knowledge for Phased Rollout 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
This paper explores how projects that can be considered as repeatable and replicable in a sequence of deliverables over a period of time can be planned and executed (rolled out) in a way that maximizes knowledge transfer and production from one execution to the next. A variety of management disciplines were reviewed to understand the way that project and program rollouts are treated in IT, product development, organizational learning, and manufacturing literature. These reviews enabled the development of a conceptual model to describe how planning and implementing the rollout of a product or service can be more knowledge-focused and an accompanying capability maturity model. Several examples from real life, including one experienced by one of the authors, were used to illustrate and test the conceptual model. The model integrates knowledge management, change control, and planning in a way that can develop organizational learning and the authors assert that this can improve productivity and both tangible and intangible returns to those that follow the model. An accompanying capability maturity model is also presented. The conceptual model, while initially tested against several real-life cases, needs to be tested against a new set of projects as an action learning research project in order to more fully develop knowledge of the dynamics of learning and improvement in rollout projects. The implication is that when project managers undervalue important learning aspects of phased rollouts, they may miss important organizational learning opportunities.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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