The Impact of International Risk on Financial Sector
Bibliographic record
Abstract
Although the MENA is considered a high-risk region, the effects of the political risk on business, in particular financial sector, have not received enough attention in previous studies. This paper, therefore, critically examines international risk affecting the financial sector in Jordan. Such an investigation and the institutions’ responses to international risks may compensate the previous gap. In line with the aim of this paper, the phenomenology philosophy was adopted and the inductive approach was followed. The entire target population of the 64 financial institutions which are listed at Amman Stock Exchange for the year 2013 has been studied. The key risks that have affected the financial institutions operating in Jordan include: the Gulf War, the international financial crisis and the Arab Spring. Not all of the financial institutions, however, are affected equally by these risks. Differences among the financial institutions in risk exposure to these risks arise from the differences of their scope and scale. These differences, therefore, have led to differences in financial institutions’ responses. A number of strategies and policies have been adopted. The paper represents one of the first such investigations in the context of Jordan. The research findings provide a review and understanding of how financial institutions in developing countries respond to particular international risk.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".