Bibliographic record
Abstract
The Saudi Arabian economy has been based on the exports of petroleum products for the last years.External environment factors affect demand and the revenue of exports of the petroleum products of Saudi Arabia.There is a need to develop other sources of economic contribution to hedge against the revenue fluctuations of petroleum exports due to the external business environment or uncontrollable factors.This research aims to focus on measuring the non-oil economic contribution to the GDP of Saudi Arabia, rather than the oil revenue contribution, comparatively.The secondary data used in the study were obtained from the Saudi Arabian Monetary Authority (SAMA) for the years 2010-2024.FBI (Fixed base index numbers), CBI (chain base index numbers), CV (Coefficient of Variation), and ATGR (Average Trend Growth Rate) are applied to get the trend and abnormality of variability of data.FBI measures the change in the value of a variable in the context of one fixed base year, while CBI measures it in the context of the previous year.ANOVA (Analysis of Variance), EWATGR (Effective Weighted Average Trend Growth Rate), and PEWATGR (Proportional Effective Weighted Average Trend Growth Rate) are applied to get the significant differences among the growth trends, growth rate of unequal components of a variable, and proportional contribution.In this study, data from a few categories are compared using a clustered column chart.It is inevitable to focus on agriculture, forestry, and fishing; other mining and quarrying; electricity, gas, and water by the Ministry of Environment, Water and Agriculture; and the Ministry of Energy of Saudi Arabia to enhance the quantitative proportional contribution for non-oil GDP to shift the oil economy to a non-oil economy.The Saudi government may apply lower rates of taxes on the goods and services identified as zero-rated, to enhance the proportional contribution of the net taxes on products.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".