A Research on the Innovation of Science and Technology and the Escalation Elasticity of Service Outsourcing Industry:An Analysis Based on the Data of Innovative Cities and Service Outsourcing Cities
Bibliographic record
Abstract
Service outsourcing industry is high-tech, high value-added and high-end services and transfer of R D of global industry structure upgrades the inevitable trend. This article, from the perspective of innovation of science and technology, selects 16 national innovation cities and service outsourcing model cities as the research sample. Through principal component analysis and stepwise regression, it is concluded that factors that impact the service outsourcing industry to upgrade the important innovation of science and technology include enterprise RD expenditure for product sales income and GDP per capita, potentially driving tendency of the innovation of science and technology factors including college students enrollment, professional and technical personnel account for the proportion of expenditure on employment, RD and GDP ratio. On this basis, this paper, by constructing a regression equation to calculate sensitivity analysis of the independent variable elasticity index, proposes that China tap the potential for professionals to increase the strength of investment in science and technology to promote the upgrade of the service outsourcing industry, according to the calculation results.
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 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 | 0.011 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.019 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".