Measuring the Effect of the Internet on Economic Growth: The Case of Saudi Arabia
Bibliographic record
Abstract
The purpose of this study is to examine and assess the impact of the Internet on economic growth in Saudi Arabia. Various studies show that there is a relationship between the growth rate of GDP and the Internet, as estimated by Internet user numbers. In this paper, the ordinary least squares (OLS) model is utilized to study the economic impact of Internet Access from 1994 to 2018, which has had a profound effect on the market structure of many sectors and Saudi’s global macroeconomic performance. The study constructs a model to investigate any significant impact of the Internet on the Saudi economy. Finally, this paper suggests that an understanding of the role of the Internet is essential for policymakers who plan to promote new forms of economic growth in the future. To take a long-term view implies working on technologies that could improve the economy and people’s lives by creating a technological ecosystem in and around Saudi Arabia, along with other major economies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".