Textual and Quantitative Research on China’s Action Plan for Promoting the Development of Big Data From the Perspective of Policy Tools
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
The research on the development of big data from the perspective of policy tools, to help policy makers look for policy tools that can provide guidance and support for the development of big data. The research is of significant theoretical and practical value for promoting development of big data and realizing the strategy of data power country. Using content analysis method and quantitative analysis methods, this paper evaluates and discusses China’s action plan for the development of big data from the perspective of policy tools. Government uses more supply-side and demand-side policy tools to stimulate and support the development of big data. Nevertheless, the stage of technology research and development stage has not been given enough attention. To improve and update China’s action plan for promoting the development of big data, policy tools system needs to be integrated or coordinated with data powerful country’s value-chain.
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.
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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.001 |
| 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