Analysis of the opportunities to implement the BIZ-TRIZ mechanism
Bibliographic record
Abstract
Abstract Aiming to strengthen cooperation between scientific entities and enterprises and to overcome related obstacles, the authors propose to create a mechanism of incentives called BIZ-TRIZ, which is an abbreviation for “TRIZ for Business”. This mechanism is used to support cooperation between scientific entities and companies. Close cooperation is achieved by implementing R&D&I services, which is the responsibility of the scientific unit operating for the benefit of the companies involved. Research services are used together with the scientific instrument that reflects achievements in the modern theory of innovative problem solving (TRIZ). The analysis was made using the Maritime University of Szczecin and SME-type companies as an example. This paper describes the basic assumptions concerning the implementation of the BIZ-TRIZ mechanism. Also, it presents the use of SWOT analysis, needs/stakeholder analysis and risk analysis for the implementation of the BIZ-TRIZ mechanism. The paper describes preventative actions for the most important implementation risks and discusses the results of the analyses. Finally, it introduces the main conclusions regarding the purpose of implementing the BIZ-TRIZ mechanism.
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 | 0.032 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".