Bibliographic record
Abstract
In accessing inequality all over the world, recent research by Fischer has found an interesting result: without regard to population size, incomes in poor countries grew slower than incomes in rich countries, implying that the poor are falling behind and that cross-country inequality is getting worse. However a population weighted analysis indicates that the poor are growing faster, which implies both catch-up and narrowing inequality. An attempt is made in this paper to examine whether the same pattern of inequality applies to the case of provincial comparison in China. Our finding shows, even after taking into consideration of population and using the improved accessing method, we find no evidence of less inequality across all the people. Key words: inequality, population weighted, convergence, divergence Resume: Sur l’inegalite de l’acces dans le monde entier, les recherches recentes effectuees par Fischer ont trouve un resultat interessant : sans relation avec la taille de la population, les revenus dans les pays pauvres croissent plus lentement que dans les pays riches, cela implique que les pauvres sont en retard et que l’inegalite transnationale devient de plus en plus grave. Neanmoins, une analyse qui met l’accent sur la population montre que les pauvres augmentent plus rapidement, qui implique le rattrapage et l’inegalite diminue. L’article present tente d’examiner si le meme modele d’inegalite est applicable a la comparaison provinciale en Chine. Les resultats montrent que, meme en mettant en consideration la population et en utilisant la methode d’acces amelioree, on n’a pas trouve la preuve d’une inegalite decrue a travers toute la population. Mots-Cles: inegalite, population qui importe, convergence, divergence
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".