Empirical Research on the Relationship between the Subdivided Indicators of the Sustainable Development Rate and the Enterprise R&D Input
Bibliographic record
Abstract
This paper divided sustainable development rate into return on total assets, equity multiplier, retained earning ratio，and tested their relationships with the R&D input. By applying the cross-section regression method, it builts up the regression model through the empirical research on 93 listed high-tech companies in electronic, medical, and new material industries, in which the influence of those three indicators on the R&D input was tested. The results implied that the coefficient of return on total assets, retained earnings ratio, and the equity multiplier was 0.101, 0.0018 and -0.007 respectively. Finally, this paper analyzed those coefficients and proposed the comprehensive solution for how to take rational financial action to promote the R&D input in different conditions. Key words: Sustainable Development Rate, Return on total assets, Equity Multiplier, Retained Earning Ratio, R&D Intensity Resume: L’article present divise le rythme du developpement durable en rendement de l’actif total, multiplicateur de capitaux propres, ratio du rapport, et teste leur relation avec la contribution de R&D. En applicant la methode de regression en coupe transversable, il construit le modele de regression a travers les recherches empiriques sur 93 entreprises cotees de haute technologie dans les domaines electronique et medical ainsi que l’industrie de nouveaux materiaux, dans lesquels l’influence de ces trois indicateurs sur la contribution de R&D ont ete experimentee. Les resultats impliquent que le coefficient de rendement de l’actif total, le ratio du rapport et le multiplicateur de capitaux propres sont respectivement 0.101, 0.0018 et -0.007. Finalement, l’acticle a analyse ces coefficients et propose une solution synthetique favorable a la prise des politiques financieres raisonables afin de promouvoir la contribution de R&D dans de differentes conditions. Mots-Cles: rythme du developpement durable, rendement de l’actif total, multiplicateur de capitaux propres, ration du rapport, intensite de R&D
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".