Bibliographic record
Abstract
This chapter deals with the important issue of “kicking off” a program. It is critical that a program begins in the right way. Having clear business goals and a professional work environment in place will provide a first-class atmosphere. PROGRAM INITIATION The start of a program is critical to its success because it is at this time that the project manager brings together the program team and set up the environment in which it will work. Because the IPTs are the most valuable program resource, ensuring that its members receive the proper training, a clear understanding of the program's goals, and the plans for achieving those goals are closely linked to the final success of the project. Recall from Chapter 8 that during project initiation you can resolve project structuring issues now better than at any other time during the program. Establish program goals Program and IPT goals should be established to ensure that the customer's needs are met. The first step in this process is to define the product functions and features, design-to-cost goals, and key milestones. Figure 9-1 illustrates the design-tocost model. Notice how the integration of elements will drive the eventual cost. Investments in process capabilities can be as important to the final cost structure as the design itself. This model helps to establish the mission and focus of the IPTs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.040 | 0.025 |
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".