Influencing Factor of Investment in China from Perspective of City Space
Bibliographic record
Abstract
This paper discussed the spatial distribution features of the city-level investment in China by applying the Moran's I variable. Further,the spatial statistic empirical research on the city-level investment was conducted. The results were as follows: First,there were significantly positive correlations between investment and GDP,fiscal expenditure,finacial capital,human capital,that meant the increase of investment in a region would be drived by cumulation of these variables in the neighboring region. Second,the results of analyzing all cities in China showed that the scale of investment were affected by GDP,fiscal expenditure,finacial capital. In addition,the spatial correlation coefficient of SLM model was significant. It showed that spatial correlation was also an important factor. The impact of human capital on the scale of investment wasn't significant. Third,there were obvious differences in the influencing factor of investment in easten,central and western cities in China. In the east,the main influencing factors of investment was GDP. In the middle it was GDP,fiscal expenditure,financial capital. In the west it was GDP and fiscal expenditure.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".