A Concept Map for Design Method Experiments in Product Development – A Guideline for Method Developers
Bibliographic record
Abstract
Due to the emergence of experiments in product development and yet still missing systematic adjustment of the generic experimental design procedure to test design methods. a facilitation is required. A concept map to depict the most important elements to consider when validating design methods in an experiment was developed systematically to guide method developers. Combining literature findings with the analysis of six cases in academia and an intensive exchange of researchers between different institutions resulted in the concept map. Future research potentials in the definition of variables. gaining objective results and managing the required tools are outlined. The concept map is available online for further feedback from the community.
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 | 0.190 | 0.236 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.019 | 0.016 |
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".