Exploring Migration Economy – Understanding the Loss of the Arab World
Bibliographic record
Abstract
Migration economy has been getting more attention in the last two decades and specially in the last few years due to the huge migration movements around the world, but specifically from South to North. Lots work have been written about the economics of migration and how they create positive and negative impacts on the hosting countries and societies, however few literatures focused on exploring the loss of the migrants’ countries of origin and quantifying the benefits for the hosting countries.The Arab world suffered lately more than any other region in the world lots of traumas that led to make its push factors much more than its pull factors for people with the ambition of change and creating a legacy. In this study, we shall explore the level of loss that Arab world have reached and what is the foresighted migration decisions for pulling successful people in the future, especially if the same conditions and practices exist in such countries. The data collected for seventy screened successful Arab Migrants helps to clarify what type of precious human capital the future carries towards the hosting countries. A tabulation of the level of the contribution of the successful Arab migrants is evaluated and lead for sharper conclusion about the value of these precious assets.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".