Suggestions for Improving Initiation of Pipeline Projects
Bibliographic record
Abstract
Effective and successful project management of today’s pipeline projects is a challenging and complex task. For the most part, these complexities are not due to technical issues, but pertain to “soft management issues” (communications, team building/alignment, stakeholder management, etc.) that must be immediately and aggressively addressed during project initiation. That is, a key success factor for these projects is setting up for success, upfront at the very beginning, and ensuring the right resources and processes are in place to manage the “soft side” as the project progresses. This includes initiating continuing processes to check the status of the project team climate, interaction health, and development of a “risk sharing/monitoring” culture.
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 | 0.037 | 0.134 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.050 | 0.018 |
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".