Bibliographic record
Abstract
This paper estimate and compare the regional index in China. Along with the economic development, many researcher interest the cultural factor. The cultural factor plays an important role in regional economic development. But the empirical research have some obstacles because of estimation limit. This paper try to estimate the regional cultural index in China, in the base of UNESCO, Canadian Statistics and New Zealand Statistics. Most of empirical study estimate each sector, and there are no one dimension index, this paper establish one dimension cultural index, include five first degree sector, the cultural supply, the cultural demand, the cultural support, the cultural heritage, the social and economic condition. And then this paper use AHP method calculate the sectoral weight, the cultural supply and the cultural support have higher weight, and another three sector have lower weight respectively. Each first degree sector include three second degree sector, so the cultural index have the fifteen second degree sector. This paper estimate the 31 regions cultural index, in 2001, and 2005-2009. The result finds that the regional cultural index have some disparity, and the average of cultural index increased by year. The result also finds that the cultural index and the regional economic development have strong relation, the regional economic development level more high, the cultural index also more high.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".