Identifying Strategic Development Objectives for African Countries Using Dominance-Based Rough Set Approach: The Poverty String Theory
Bibliographic record
Abstract
The objective of this article is to expose the results of a research using Dominance-based Rough Set Approach (DRSA) to help African countries and international organizations (both non-governmental organizations and governmental organizations), to identify economical, sociological, political and technological strategic objectives for international development. We hope that the results of this research will aid politicians and leaders to prioritize African countries strategic development objectives according to political, economical, sociological and technological (PEST) needs. In this study we use 23 various indicators to classify all the African countries according to the following three different categories: [A] African countries that are doing well according to the selected indicators; [B] African countries that need support to acquire category A status; [C] African countries ranked the lowest and needing special support with regard to the criterion or criteria considered. The three categories are delimited by tertiles obtained from the average ranking of countries. The chosen criteria are measured in order to provide decision rules based on this classification. These decision rules thus focus on the political, economic, sociological and technological needs of countries with respect to improve their development and classification. We strongly believe that by targeting these identified needs, this research will help the development of African countries, target and prioritize International funding, evaluate economic growth and sociological improvements. Our results, from both the correlation matrix and DRSA, clearly demonstrate that top priority should be given to analphabetism, school life and reducing the amount of adolescents pregnancies in order to improve both economically and sociologically. Also, our analysis of the African map belonging to the overall classification results, puts the light over the fact that most countries in category C are, geographically connected to one another, what we named the “Poverty String’’. This is the first research of a series of three articles using DRSA in identifying strategic objectives for international development. The second research will discuss the use of DRSA to identify strategic objectives for Bosnia Herzegovina as a potential candidate to the European Union. The third research will use DRSA to help define poverty for all the United Nations countries and propose decision rules for international development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".