The Impact of Free Trade Zones on a City's Innovation Capacity
Bibliographic record
Abstract
Facing the complex international and domestic situations, the new round of scientific and technological revolution and industrial transformation, and the new expectations of the people, we must continue to advance the reform. Openness is a distinct feature of China's modernization. We should promote reform through opening up, leverage the advantage of our super-large-scale market, enhance our capacity for opening up in the process of expanding international cooperation, and build a new system for a higher-level open economy. High-quality development requires new theories of productive forces to guide it. The essence lies in scientific and technological innovation and revolutionary breakthroughs in technology. Science and technology are the primary productive forces, and innovation is the primary driving force. So, how do free trade pilot zones affect a city's innovation capacity? This is a topic of great research significance. This paper takes 222 cities, including those where the free trade pilot zones are located and other cities, as the research objects. Based on the panel data from 2012 to 2023, an empirical regression is conducted using the multi-time point differdifference method. Under the double fixed model, the influence mechanism and heterogeneity of the establishment of free trade pilot zones on the innovation capacity of the cities where they are located are examined. This paper adopts the multi-time point differin-differences method to study the impact of free trade zones on urban innovation capabilities. The research results provide policy inspirations for the establishment of free trade pilot zones and the improvement of urban innovation capabilities.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".