Creativity and Innovation in SPI: an Exploratory Paper on their Measurement?
Bibliographic record
Abstract
Abstract In recent years, some software organizations have been successful at improving their maturity level, thanks to the successful application of methods and techniques which help them to achieve betterprocesses actually covered by such people models?performance and more consistent production processes. Models such as the Sw-CMM (and its evolutions and derived models) have provided roadmaps to process improvements. Creativity and innovation have been placed at Level 5 of the CMMI and the -CMM respectively. AP suggestion is made in this paper to consider creativity and innovation management earlier on in such SPI models. Also in this paper, we propose, in an exploratory way, a method for mapping, tracing and measuring creativity, based on two entities: the CA matrix and the Creativity Indices. 1. Introduction People cannot be managed as a second-level asset, as strongly emphasized in quality models like the Malcolm Baldridge Model [21] and the EFQM [9]. The Malcolm Baldridge National Quality Award 2001 criteria, for example, take into account 375 points of 1000 (37.5%), split among Enabler processes (Human Resources Focus: 85 points and Customer and Market Focus: 85 points) and related Results (respectively, 125 points plus another 80 points). For its part, the EFQM Excellence Model, with 1999 weight criteria, takes into account 18% of the total number of points, the People Enabler being responsible for 9% and the related results for another 9%. Similarly, the Balanced IT Scorecard (BITS) designed by the European Software Institute [15][26][27] and the AIS BSc [10] have both added a fifth perspective,
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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 | 0.013 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".