Relationship between Technology and Economic Growth
Bibliographic record
Abstract
The study analyzes the relationship between technology and economic growth in Jordan during 2009–2018 and the data are treated via Views E program. ARDL methodology are used. Results showed a co-integration relationship between the study variables (computer use in general; computer use at work; and computer use in education, training, and economic growth) and the results presented that the deviation from long-term equilibrium is corrected using an error correction model which long-term corrected as a percentage correction (−0.06) each year from the short-term to the long-term and showed the results of the structural stability test of the (ARDL) model. It is a structural stability test for long and short-term coefficients, which showed that the data used in this study are free from any structural changes has stable parameters over time The study also used CUSUM's Squerse test, where the test results showed that the study model used is economically good and can be relied upon to anticipate economic solutions in Jordan according to the situation in the coming years, and among the most important recommendations of the study are the following: the need to encourage the use of technology in work, education, and training, and the need for expansion in using these technological means as a gateway to the digital economy and the digital state.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".