Scope Creep Monitors Level of Satisfaction, Cost of Business and Slippery Slope Relationships Among Stakeholders, Project Manager, Sponsor and PMO to Execute Project Completion Report
Bibliographic record
Abstract
Scope creep is a change which is an update or addition to the whole or a part of project has been requested when the project is running on significantly an underway. Scope creep increases work with level of satisfactions off as well as any one would expect, but over time the project seems to get bigger and bigger while his or her price remains the same. The stakeholder either seems to think that the “extra work” is within the scope of the original agreement, or simply doesn’t realize that he or she is asking for more than was originally agreed. Either way, the project is losing money. Scope creep is a slippery slope and can be difficult to recover from. Once project manager accepts scope creep from one client s/ he is setting a precedent for the rest, and although s/ he may not have to physically hand over money as a result of scope creep, the effect is essentially the same. More time spent on a project than sponsor anticipated puts s/ he out of pocket.
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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".