Exploring the path of regional industrial upgrading and talent supply driven by new quality productivity
Bibliographic record
Abstract
Based on the realization path of new quality productivity on industrial transformation and upgrading as well as talent supply, this paper carries out theoretical analysis in three directions, namely, direct effect, indirect effect and non-linear characteristics, and puts forward relevant research hypotheses. The panel data benchmark regression model, mediation effect model and threshold regression model are constructed respectively to verify the proposed hypotheses. The panel entropy method is used to measure the level of new quality productivity and carry out related research on the driving of new quality productivity. The partial differential decomposition of the spatial Durbin model shows that the direct and indirect effects of the new quality productivity are significantly positive at the 5% and 1% levels, respectively, indicating that the new quality productivity can promote the upgrading of industrial structure in the theoretical provinces. Introducing the variable of technological innovation for the mediation effect test, by observing columns (1) and (2), the regression coefficients of new quality productivity on technological innovation and new quality productivity on industrial structure upgrading are both positive at 0.2484 and 0.2048, respectively, which indicates that regional technology plays a mediating effect in the influence of new quality productivity on industrial structure upgrading.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.011 | 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".