Evaluating the Trend of Trade between the Same Categories in the Human Development Index (HDI)
Bibliographic record
Abstract
The first report of the human development index was at 1990, it is an important tool to evaluate the progress in the countries by GDP growth and social issues. Is the classification of HDII enough to explain the international relation between the categories of the countries that belongs to the same level of HDI? Giving evidences to grow the trend of trade between the nearest GDP countries, it is a formula of HDI and trade, the paper divided into three sections, First: The distribution of the human development index in the global and the areas that appeared; Second: Evaluate the distribution of the human development index in the top ten world; Third: Export between the top 10 HDI countries. Is really the full assessment of human development need a much broader set of indicators than the HDI alone? Can add the export rate to discuss the impact on the economic growth and the increasing on the income at the top ten countries at HDI? (Ranis, Stewart, & Samman, 2006).
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".