Technological upgrading and decent work in the manufacturing sector: evidence from seven coastline provinces, China, 2002-2014
Bibliographic record
Abstract
This research examines the relationship between technological upgrading and decent work in the manufacturing sector in China. By analyzing data from seven selected coastline provinces (Jiangsu, Guangdong, Zhejiang, Shandong, Fujian, Guangxi, and Hainan), both technological upgrading and an increase in decent work have been observed between 2002 and 2014. Decent work, however, is not distributed evenly among Chinese citizens. The average wage is identified as a key decent work indicator, and selected as the dependent variable in the econometric regression model. The impact of technological upgrading on the average nominal wage is estimated by a time-series-cross-section regression model using provincial level data from 2003-2014. Regression results indicate that technological upgrading accelerates wage growth in the manufacturing sector. My suggestions for future government development strategies are, to firstly, revise the Hukou system to promote equal opportunities for all citizens, and secondly, offer preferential policies to attract more FDI and encourage domestic R&D... .
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".