Bibliographic record
Abstract
This research aims to find economic and social solutions through the development of industry in the Arab countries after suffering, for decades, from the lack of interest in order to achieve industrial development and social security, where most of the Arab experiences failed or did not succeed compared to many experiences in the emerging industrial countries. This research addresses the reasons for this failure to achieve industrial development and its effects on economic and social development and contribute to solve the problem of unemployment in most Arab countries. It also contributes to find solutions for industrial development and social security through some proposals. The results of this study also confirmed the existence of policies focusing on the extractive industries, while the manufacturing industries should be interested in achieving industrial development, reducing the unemployment rate and advancing industrial development. The statistical approach and the descriptive and analytical approach were adopted to approach and address the problem of unemployment in the Arab world, which is one of the highest in the world. In the research summary, the importance of investment in the field of manufacturing industries, which depends on the human density, so that the greatest possible number of job opportunities can be created, thus contributing to addressing this problem which threatens the security and stability of most Arab countries. Investment in the food industry, furniture industry and other light manufacturing industries can be a solution to the phenomenon of unemployment in the Arab world, in contrast to industries with a capital density associated with extractive industries.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".