Recommendations to improve the usability of research results as reference system elements addressing corporate engineers, researchers, and policymakers
Bibliographic record
Abstract
Product engineering is a highly complex process faced with many challenges. To meet today’s challenges of society, such as climate change, energy production and demands, and demographic shifts, and to achieve economic success for companies, technical solutions in products and systems must evolve and advance. Hereby, university research provides a potent source of cutting-edge technologies and knowledge that companies can use as input to advance their solutions. However, many challenges and barriers hinder the process of transferring new technologies, concepts, and knowledge from research to corporate engineering. In this article, we present 22 recommendations to improve the usability of research results for the activities and processes of corporate product engineering. These recommendations address the three relevant target groups: (I) corporate engineers and companies, (II) researchers and research facilities, and (III) funding agencies and (research) policymakers. First, we offer recommendations for corporate engineers and companies to integrate research results more efficiently. Second, we present recommendations for researchers and research facilities to support the promotion and transferability of their research results. Third, we provide recommendations for funding agencies and (research) policymakers to positively influence the usability of research results in corporate engineering. We expect that implementing one or more of our recommendations will enhance the efficiency of knowledge and technology transfer into corporate product engineering. We anticipate this will lead to faster technological advancements for companies and social benefits by addressing today’s major challenges more effectively.
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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 | 0.203 | 0.602 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.016 | 0.029 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.018 | 0.013 |
| Insufficient payload (model declined to judge) | 0.026 | 0.024 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".