Arabia versus Persia: Is This What the Arab Spring Ended with?
Bibliographic record
Abstract
The sectarian violence in the Middle East has changed the face of the Arab Uprisings and has extended violence between Sunni and Shiite Muslims to a wider scale. The current investigation explores economic and social implications of the Sunni-Shiite Muslim tension and competition to control the Middle East. The study aims to examine the influence of demographic and socio-physiological variables on feelings of animosity Arabs express towards Iran and purchasing Iranian products. The research design is quantitative. Data were collected from 108 Arab Sunni Muslim Jordanians who live in the capital of Jordan, Amman and the northern Irbid city over a period of 4 weeks, June, 2014. The results showed that younger Jordanians and Jordanians who express high level of internationalism hold less feelings of animosity against Iran than their other counterparts. Also, it was found that Jordanians’ feelings of animosity towards Iran have been translated into unwillingness to purchase Iranian products. Limitations of this research overall are related to employing a convenience sample and the relatively small sample size. Feelings of animosity Arab express toward Iran are not alarmingly high, nonetheless, such negative feelings should not be ignored by neither Iranian global marketers nor by Arab local marketers. To the best of the author’s knowledge, there is a death of studies that explored the social and business implications of the current Arab Spring events.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".