Structural Bonus of Factor Productivity in China’s High-Tech Industry: A Cross-Sector, Cross-Province, and Cross-Ownership Study
Why this work is in the frame
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Bibliographic record
Abstract
The evolution of Chinese high-tech industry labor force and capital structure is analyzed using a shiftshare technique. Contributions from cross-sector, cross-province and cross-ownership flows of factors to productivity growth were assessed. Cross-sector labor force flow produced “positive structural bonus”, cross-sector capital flow produced “negative structural bonus”, cross-province labor flow produced “negative structural bonus”, cross-province capital flow produced a “positive structural bonus”, crossownership labor and capital flow produced “positive structural bonus.” Implications are decrease intervention in operations, allow free factor movement among sectors, provinces, and ownership; improve capital market and improve labor market to channel skilled workers into the high-tech industry.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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