Bibliographic record
Abstract
This book is intended to help you implement a more rigourous approach to the practice of engineering management. In our consulting work, we have seen many attempts to improve this management process. Mostly we have been called in when previous initiatives have failed. We believe that these initiatives have failed for one simple reason. They were narrow, one-dimensional solutions to problems that had many facets. Additionally, the people who offered the consulting services quite often had very little practical background in engineering. In this book, we will look at the engineering process from a holistic approach. Typically, we see the scenario play out as follows: a firm finds that its development projects are taking too long or costing too much money to complete. A senior manager in engineering has read a book, attended a course, or acquired considerable experience in one particular approach and recommends that the organization simply implement this new way and the problem will be solved. In the best case, a year later the performance in the one area at which the solution was aimed has improved, but there is no broad-based financial measure of improvement. In the worst case, the organization is in upheaval with pockets of resistance firmly entrenched against the change. The reason for this failure is the application of improved methods in isolation from one another. These solutions typically originate from one of six bodies of knowledge.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".