Proceedings of the First International Research Workshop for Process Improvement in Small Settings, 2005
Bibliographic record
Abstract
The first International Research Workshop for Process Improvement in Small Settings was held October 19-20, 2005 at the Software Engineering Institute in Pittsburgh, Pennsylvania. Attendees from Australia, Canada, Chile, China, Germany, Ireland, India, Japan, Malaysia, Mexico, Spain, and the United States discussed the challenges of process improvement in small and medium size enterprises, small organizations within large companies, and small projects. The presentations addressed starting and sustaining process improvement, qualitative and quantitative studies, and using Capability Maturity Model Integration (CMMI), Agile, Modelo de Procesos para la Industria de Software (MoProSoft), International Organization for Standardization (ISO), Quality Function Deployment (QFD), and Team Software Process (TSP) in small settings. The workshop also had working groups that discussed issues unique to small settings, such as regional support centers and process improvement "on a shoestring." This report includes the papers from this workshop and presents conclusions and next steps for process improvement in small settings. This report also contains the workshop breakout session results.
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 | 0.015 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.073 | 0.022 |
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".