Grey relational evaluation of innovation competency in an aviation industry cluster
Bibliographic record
Abstract
Purpose The purpose of this paper is to propose an evaluation model for evaluating the innovation competency in the Yanliang Aviation Industry Park, which is a typical example of an aviation industry cluster. Design/methodology/approach A subjective weighting method based on the order relation is used to determine the index weights, which are utilized in grey incidence analysis to measure the innovation competency of the aviation industry cluster. Findings The application of the index methodology to the Yanliang Aviation Industry Park demonstrates that the industry cluster possesses a strong innovation competency, as well as the feasibility and practicability of employing this approach. Practical implications The method introduced in the paper can be used to solve practical problems. Moreover, it provides potential support for the development of the aviation industry in the future. Originality/value In this paper, the high technology aviation industry, which now plays a strategic industrial role in China, is systematically studied by using a new methodology based on grey systems. Additionally, a subjective weighting sequence model founded upon a grey relational analysis is utilized in place of the analytic hierarchy process (AHP).
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".