EFL Learning and Vision 2030 in Saudi Arabia: A Critical Perspective
Bibliographic record
Abstract
Vision 2030 is a key economic transformation plan for Saudi Arabia conceived in 2016. It envisions the shift from oil to a knowledge-based global economy. Along with this historical event, Saudi Arabia undergoes another historic transformation, i.e., the strategic growth of EFL learning in the domain of education in the second decade of the twenty-first century. Since economy and education go hand in hand, scholars wonder whether EFL learning induces Saudi Vision 2030 or vice versa. The relation between them is causal by nature, but the order of effect is inexplicit. If Saudi Vision 2030 motivates EFL learning, then its present situation is inadequate for the fulfillment of the Vision. If not, then how the inadequate EFL competency of Saudi student can contributes to the visionary transformation of Saudi Arabia. Hence, the problem is an essential concern in academic understanding since its resolution redefines both EFL learning and Vision 2030 of Saudi Arabia. The study uses qualitative research on the existing scholarly books and articles on EFL learning and Saudi Vision 2030 to understand the causal order of these phenomena, to illustrate the present condition and explain interrelation of EFL Learning and Vision 2030 of Saudi Arabia.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.025 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| 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".