Validation issues of a performance management system for design: three case studies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose This study aimed to discuss issues related to the process for validating a performance management system for design (PMSD) in three product development companies. Design/methodology/approach The use of multifunctional groups becomes important because it favors viewing the organization as a whole, thereby reducing existing gaps between segments of the company. To support this study, focus group research was used. Findings Viewing design as a resource that contributes to increased competitiveness offers companies benefits, such as improved performance measurement. This measurement is based on indicators and, to be useful, an indicator system should stimulate the company's interest. In addition, the present study made it possible to conclude that the validation process is essential in preimplementation stages because validation allows the PMSD to be adapted to bring it closer to the reality of companies, thus increasing the chances of success during the implementation stage. Originality/value Validation of the metrics from the perspective of senior management enabled critical analyses of the applicability of the PMSD, as well as its suitability and approximation to the reality of businesses, by selecting the most relevant data.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it