The Relationship between Happiness and Economic Development in KSA: Study of Jazan Region
Bibliographic record
Abstract
It is generally assumed that happiness is a major source of motivation. Though economic growth remains the main goal of all nations, nowadays a society with happy people is an objective to aim at. From this raises the issue of the relationship between happiness and economic growth.In this paper, researchers try to find how people’s happiness influences GDP and economic development. But before that they focused on the question of how happiness is achieved. In order to do so, we start by directly asking Jazan’s habitants through a survey about the source of their subjective well-being and what themselves say about what makes them happy. Then how do these sources of happiness influence their economic performance and participation in GDP growth. The sample’s answers have been formed according to the quintuple likert scale. We used the statistical technique of Cronbach’s Alpha to measure the credibility of the sample’s answers.Researchers used the analytical descriptive methodology in order to analyze all collected data. Results show that social factors are the most important factors drivers of happiness and therefore influencing positively individual’s contribution in economic development of Jazan region. However, economic factors and political factors show that social factors are the main sources of happiness for our sample leading to a better economic development.
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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 | 0.000 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".