Establishing a generic and multidimensional measurement repository in CMMI context
Bibliographic record
Abstract
We propose a measurement repository for collecting, storing, analyzing and reporting measurement data based on the requirements of the capability maturity model integrated (CMMI). Our repository is generic, flexible and integrated, supporting a dynamic measurement system. It was originally designed to support Ericsson Research Canada's business information needs. Our multidimensional repository can relate measurement information needs to CMMI processes and products requirements. The data model is based on a hierarchical and multidimensional definition of measurement data. It has been developed based on the concept of a data warehouse environment. Reporting features are based on the definition of queries to on line analytical process (OLAP) cubes. OLAP cubes are created as materialized views of the measurement data, and the user functionalities are implemented as analytical drill-down/roll-up capabilities and as indicator and trend analysis capabilities.
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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 | 0.031 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".