Two for the Road: Measuring Regional Disparity and Agglomeration in Human Development Level and Transportation Infrastructure
Bibliographic record
Abstract
As the core element of social-economic development in the Qinghai-Tibet Plateau, transportation dramatically shapes the scale, type, and intensity of human activities. First, this study utilizes night light data and kilometer-grid population data to construct night light development index (NLDI) and to evaluate the human development level at the county scale. Then, based on the complex transportation infrastructure data, the weight assignment method is adopted to create transportation infrastructure influence degree (TIID), which is used to evaluate the location conditions of the counties. Finally, bivariate spatial autocorrelation is utilized to analyze the effect of regional conditions on the county-level human development variation. The results show that (1) NLDI is verified to assess differences in the level of human development among counties in Qinghai-Tibet Plateau and to overcome the difficulties of systematically and integrally obtaining socio-economic statistical data. The pattern of human development level in Qinghai-Tibet Plateau presents a “core-periphery” spatial structure with the transportation network as the axis. (2) On the whole, with the improvement of location conditions influenced by transportation infrastructure, the spatial aggregation of human development level is constantly improving, and the spatial disparity continues to decrease. (3) Locally, four spatial interaction patterns of high/low clustering are recognized and analyzed. It reflects the complexity and spatial heterogeneity between transport infrastructure construction and human development level in the Qinghai-Tibet Plateau.
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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 | 0.000 | 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 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".