Digital Divide and Income Inequality: A Spatial Analysis
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
A spatial quantile regression model, which can fully describe the distribution characteristics and spillover effects, is applied to explore the effect of digital divide on the income inequality. Firstly, the estimation results based on the full data set reveal that income inequality is positively spatial dependent across regions, and the Internet has a significantly positive effect on income inequality. Secondly, the entire data set is divided into two groups based on income, i.e., high income countries and low income countries. The estimation results of two groups are quite different. The income inequality were positively spatially correlated among neighbouring countries in high-income countries but negatively in low-income countries. On the other hand, the Internet usage exacerbate income disparity in low-income countries but improve income inequality in high-income countries. The results also show that increasing school enrollment can alleviate income gap especially in low-income countries.
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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) | 0.001 | 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