Bibliographic record
Abstract
Abstract Software Productivity Center Inc. (SPC) was formed in 1992 as a nonprofit organization promoting the use of best practices and processes to increase the efficiency, reliability, and predictability of the software development process. The SPC is located in Vancouver, BC, Canada. Its clients are situated around the world with the majority in the United States. The goal outlined in the original business plan was for the center to become self‐sustaining in five years. It reached this and privatized in 1997. SPC has a few attributes that set it apart from other leading centers for software process. SPC has a business perspective. SPC advocates best practices that will have the greatest initial impact on the success of the business, while helping to put a structure in place to ensure ongoing learning and advancement of the organization and its staff. SPC has over 100 member companies that enjoy benefits such as discounts on training and products, free monthly seminars, and advice and information from SPC experts. Interaction with these member companies ensures SPC stays current with the challenges and issues facing software organizations each day. Member companies also benefit from SPC's affiliation and participation in international organizations such as the International Organization for Standardization (ISO standards), The Institute of Electrical and Electronics Engineers, Inc. (IEEE), and the Project Management Institute (PMI).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.435 | 0.379 |
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".