The Research on Performance of Industrial Innovation System
Bibliographic record
Abstract
The concept of industrial innovation system has been applied for explaining industrial development power. How about is operating ability and performance of an industrial innovation system? How evaluates performance of an industrial innovation system? Those problems raise attention of scholars and governors. The paper puts forward indicators system and method for evaluating performance of industrial innovation system. We evaluate 29 industrial innovation system applying those indicators and method. We conclude: 1st, Different industries have different operating ability and they have different performance. 2nd, If synthesizing ability of an industrial innovation system is stronger, this is not meaning its each part stronger. 3rd, There is not a model that can fit each industrial innovation system. Key words: Industrial innovation system, Principal Component Analysis, Indicators Resume: Le concept de systeme d’innovation industriel a ete applique pour expliquer la capacite de developpement industriel. Comment sont la capacite operationnelle et la performance d’un systeme d’innovation industriel ? Comment evaluer sa performance ? Ces questions attirent l’attention des savants et des gouverneurs. L’article present propose un systeme d’indicateurs et une methode pour l’evaluation de la performance du systeme d’innovation industriel. A la suite de l’evaluation de 29 systemes d’innovation industriels qui appliquent ces indicateurs et methode, on tire les conclusions suivantes : 1st, De differentes industries ont de differentes capacites operationnelles. 2nd, Le fait que la capacite synthetique d’un systeme d’innovation industriel est grande ne signifie pas que chacune de ses parts est forte. 3rd, Il n’y a pas une methode universelle qui peut s’adapter a touts les systemes d’innovation industriels. Mots-Cles: systeme d’innovation industriel, composante principale d’analyse
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".