Towards Building Academic Entrepreneurial Programs at Saudi Universities: Predicting Future Jobs in Light of the NEOM Project
Bibliographic record
Abstract
The purpose of the current study was to examine the type and form of future jobs, in light of the modern technological trends of the NEOM project. To achieve its objectives, the study utilized descriptive statistics with the Delphi method. The researchers convened a sample of ten experts in the fields of Science and Technology, Human Resources Planning, and Educational Administration and Planning, who participated in three scientific rounds. The findings from these rounds provide a suggested model for future jobs in the Kingdom of Saudi Arabia, in light of the modern technological trends of the NEOM project. This proposed model consists of twenty types of future jobs expected to achieve high degrees of importance over the next decade. Examining the study hypothesis yielded no statistically significant differences at the level (α ≥0.05) between the second and third scientific-round averages, confirming the stability of the experts' responses and their approval of this list of future jobs. The study recommends directing Saudi universities to develop academic programs to meet the needs of the NEOM project in the disciplines of Mechatronics Engineering, Using Technology in Surgery, and Software Engineering. Building academic programs with specific learning outcomes will ensure that graduates can acquire the necessary entrepreneurial skills, especially in disciplines such as Computerized Quantity Encrypted Engineering, Virtual Reality Technologies Design, and Three-Dimensional Printing Specialization. These programs will reduce acceptance of traditional academic programs that do not match the future requirements of the labor market, the Saudi 2030 Vision initiatives, and projects such as the NEOM project. The benefit from seats in more closely related programs will include the development of all programs in various universities and colleges to be entrepreneurial, supporting the new and expected technical trends in the labor market and all promising sectors of investment, such as the NEOM project.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".