Analysis of Influential Factors of Think Tanks in Chinese Universities
Bibliographic record
Abstract
Based on the spreading mechanism of think tank influence in social paradigm, this paper constructs an I-RDPS influence factor model to analyze the influence factors of think tank influence in Chinese universities. Taking the think tanks in the “2018 CTTI College Think Tank and" Top 100 College Think Tank Report "” (Guangming Daily,2019,p.16)as the research object, using the CTTI China Think Tank Index and the CNKI Database, and using python to crawl and sample this college think tank official WeChat public account data, to obtain samples Data. Using factor analysis, normal upper percentile method, and multiple regression analysis to quantify the sample data to obtain the coefficient of influence of each indicator on the influence of think tanks in Chinese universities. By analyzing the results, conducting quantitative and qualitative analysis to check and evaluate the results, and finally to make recommendations for the development of new think tanks in Chinese universities: a strategic guideline based on research results and giving full play to the advantages of distinctive disciplines; using flat modern management; and improving social networks in the evaluation system Weight of influence, attach importance to the construction of new media; build a management information system that meets the needs of college think tanks, and attach importance to resource accumulation.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".