Introducing a New Local Dependency Ratio Model for Arab Wealthy Nations: A Case Study of Kuwait Using GIS
Bibliographic record
Abstract
Dependency ratio is a simple demographic indicator measuring the percentage of non-workers over the workforce of a nation. The non-workers are composed by two segments, the youth dependent and aged dependent. These are determined by below 15 years and over 65 years respectively. The index has great utility to determine if a nation is developing or developed. However, in Kuwait, where most of the population are migrants, the index might not reflect the reality of the population. This study hypothesizes that (1) the dependency ratio differs between citizens and migrants, (2) an alternate youth ratio (local ratio) will reflect better the citizen demographic and (3) Kuwait is positioned between developing and developed countries. To test these hypotheses, the study used demographic data to create population layers in ArcGIS. The results showed that spatial demographic differences exist between migrants and citizens living in specific areas of Kuwait. The new local measurement provides a better estimation of the citizen dependency ratio, and these ratios are similar to developing countries for Kuwaitis but not for the overall population, indicating the effect of migrants. This study suggests implementing the modified youth ratio range to other rich Arabic countries. Further studies should be focused in modifying the elderly ratio.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".