Grey relational evaluation of innovation competency in an aviation industry cluster
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
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).
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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 | 0.045 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 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