Forecasting the Development of Self-Driving Technology in China by Multidimensional Information
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Due to the demand for safety and convenience in traveling, self-driving technology has developed very fast in the past decades. In this paper, a novel technology forecasting model is developed. The topic-based text mining and expert judgment approaches are combined to forecast the technology trends efficiently and accurately. To improve the reliability of the results, multidimensional information including scientific papers, patents, and industry data is considered. Then, the model is utilized to forecast the development trends of self-driving technology in China. Data ranging from 2002 to 2019 are adopted with proper data cleaning. Topic clustering for papers and patents is performed, and the hierarchical structures are constructed. On this basis, the results of technology’s evolution based on papers and patents are compared and the development trends are obtained. With these results, it is speculated that technology on “Decision” will be the next hotspot in patents. The research results of this paper will provide reference and guidance for Chinese enterprises and government in decision-making on self-driving technology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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