Research on the Measurement of the Technical Innovative Capabilities of Oil and Gas Industry Clusters and Their Factors of Influence: Empirical Analysis Based on Eight Provinces in China
Bibliographic record
Abstract
Existing studies have suggested that rich mineral resources may serve as a “resource curse” as well as a “resource blessing” with respect to regional economic development. However, the reason behind the emergence of this paradox is not clear. In this paper, we carried out an investigation of the sustainable development of oil and gas industry clusters in eight provinces of China. We studied the panel data of these industry clusters and performed quantitative analysis. By considering the effects of the technical innovation ability of the cluster on its long-term development, we showed that increasing the technical innovation ability of the cluster promoted the development of the industry, which led to a “resource blessing” situation. On the other hand, a mineral resource-based industry cluster may not survive long without technological innovation. Increasing investments in scientific research and technology development and reducing the reckless expansion of the industry cluster may lower the possibility of the occurrence of a “resource curse”.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".