The Economic Impact of the Qualifying Industrial Estates in Jordan on the Jordanian Economic Activity: A Case Study on Al-Hassan Industrial Estate, Jordan, (2000-2014)
Bibliographic record
Abstract
This study aims at exploring the impact of exports, employment,, investment size, and the number of companies in Industrial estate and the economic activity in Jordan from 2000-2014. The economic activity was represented by the Gross Domestic Product (GDP). Statistical analysis for the model of the study demonstrated that there is a positive relationship, with statistical significance, with regard to the exports of the industrial estates in Al Hassan Industrial estate. This means that the export of Al Hassan industrial estate positively contributes to enhance the economic activities in Jordan. However, the study found a negative correlation, with a statistical significance, between employments in the industrial estates of Al Hassan industrial estates with the Gross Domestic product (GDP) since most employees at the estate are from different Jordanian nationalities. The study has also found a positive correlation between the industrial companies’ size in Al Hassan Industrial estate with the GDP, but with no significance statistical significance because the inputs of productivity are imported from outside of the country based on the partnership agreement with the United States of America, which also doesn’t reflect positively on the economic activity in Jordan. The study has come up with some significant recommendations. There is an urgent necessity to enact regulations and rules for the companies in Al-Hassan industrial estate to employ local Jordanian citizens and motivate the national investment through the financial policies and adopting the policy of diplomacy of the Jordanian investor.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".