Interpretation of TRIZ Principles in a Service Related Context
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
A systematic innovation method such as the Theory of Inventive Problem Solving (TRIZ) has powerful tools that can be used to solve contradiction problems in technical or non-technical systems. The most common tool used in TRIZ is 40 inventive principles (IPs). The purpose of this paper is to interpret these IPs from a service prospective. The data was collected from many resources found in the literature. A case study was conducted to prove the feasibility of interpretative IPs. The outcome of this study enhanced the usability of the 40 IPs by including new synonyms for some principles, comprehensive descriptions, and providing suggestions and examples for each principle. The interpretative principles focused on a service process used to fulfill customer demands. An Interpretation of TRIZ tool such as the 40 IPs in a service related context improves the understanding of these principles by researchers or service designers.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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