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
China is currently seeking to transform its economic structure from a traditional industrial to a more innovative, human-capital driven, and knowledge-based economy. Our research examines the effects of three key factors on Chinese regional development in an attempt to gauge to what degree China has transformed from an industrial to a knowledge-based economy, based on higher levels of (1) technology and innovation, (2) human capital and knowledge/professional/creative occupations, and (3) factors like tolerance, universities, and amenities which act on the flow of the first two. We employ structural equation models to gauge the effects of these factors on the economic performance of Chinese regions. Our research generates four key findings. First, the distribution of talent (measured both as human capital and as knowledge–professional and creative occupations) is considerably more concentrated than in the US or other advanced economies. Second, universities are the key factor in shaping the distribution both of talent and of technological innovation. Third, tolerance also plays a role in shaping the distribution of talent and technology across Chinese regions. Fourth, and perhaps most strikingly, we find that neither talent nor technology is associated with the economic performance of Chinese regions. This stands in sharp contrast to the pattern in advanced economies and suggests that the Chinese economic model, at least at the time of data collection, appears to be far less driven by the human capital or technology factors that propel more advanced economies. This, in turn, suggests that China is likely to face substantial obstacles in moving from its current industrial stage of development to a more knowledge-based economy.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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