Study on Motivations and Cultivation of Cultural Industry Cluster in Jilin Province, China
Bibliographic record
Abstract
Cultural industry is honored as one of the industries in the 21 st Century that has the greatest development prospect. With its innovativeness, competitiveness and value added, it has become the leading edge and high end among the emerging industries in the current world, and it has also become one of the characteristic industries that have the greatest development potential in transferring the economic growth mode, promoting swift and coordinated development of regional economy in the old industrial bases. Cultural industry is playing a more and more important role in development of regional economy. Cluster is an important feature of cultural industry and cluster development is an inexorable trend in development of cultural industry. Cultural industry in Jilin Province in recent years has been rapidly developed. Nevertheless, there still exist quite a lot of problems and drawbacks. Thus, Jilin Province should find out its own characteristic factors according to its local cultural characteristics and start out from improving cultural industry policy, constructing a cultural industry cluster pattern that has comparative advantages and local features, cultivating leading key cultural enterprises and balancing cultural industrial development of all cities so as to diminish gap between different areas and improving talent security system, do a good job in taking the path of cultural industry cluster development and form cluster scale effect.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".