Bibliographic record
Abstract
It is well evidenced that development is a crucial aspect of any country. The demand of a country to be developed, it is necessary to concentrate on other aspects of economy like social and political development rather than just economic growth. In this paper our basic objective is to develop the model to predict the country’s development level on the bases of some social, economic and political indicators and also investigate the role of these indicators on development of a country. These indicators are primarily related to economic, health, education and governance. The development of a country is considered as categorical variable, the categories are already defined by United Nations Development Program (UNDP). These categories (highly developed, developed, developing and under developed) are based on Human Development Index (HDI). The data for this study is obtained for 186 countries from World Bank (WB) and UNDP for the year of 2010. Multilayer Perceptron (MLP) Neural Network Model is used for predicting the country’s level of development on the basis of economic, health, education and governance indicators, and the relative importance of these indicators in prediction. Our results show that the indicators; health, education and governance have greater effect on countries development level as compare to the economic indicators. From this investigation, it is suggested that developing and under developed countries should also concentrate on the health, education and governance to improve their development level rather than only increasing the economic indicators.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".