The Social, Cultural and Economic Impacts of Syrian Refugees on the Governorate of Maan An analytical field study
Bibliographic record
Abstract
The study aims to inform the public of the impact of Syrian refugees on the social, cultural and economic aspects of Maan Governorate. Official statistics state the existence of 1,541 Syrian refugee families in Maan, amounting to 6,607 persons at the time of the study. The percentage accounts for 2% of total Syrian refugees in the country and 6% of Maan’s population. The refugees in question do not live in standalone camps or an independent community but in the local communities. This fact facilitates their social interaction with the local community, especially given the compatibility between the two populations in terms of religion and ethnicity. Consequently, many refugees started to integrate, though partially, in the local communities. Their integration has taken place thanks to labor relations and stronger ties in the community, such as intermarriages. However, it is apparent that the two populations still have some cultural differences. Such differences incurred an invisible cost manifested in the form of direct and indirect impacts on the governorate. The abrupt demographic growth is putting more pressure on the infrastructure and public facilities, particularly education, electricity, water and health care, and especially on the labor market. In light of this situation, the study searches to find the cultural, social, and economic impacts of Syrian refugees on the Maan Governorate. The study describes the demographic, social, cultural, and economic characteristics of Syrian refugees living in Maan. It will also define the nature of social capital between Syrians and Jordanians. Additionally, the study shows that Syrians have had a significant impact on the labor market in the Maan Governorate, and the following discussions will describe the positive and negative impacts. Finally, the study will address findings concerning the Jordanian mindset and conception of how refugees affect their home country. From the findings of this study on the Maan Governorate, using both quantitative and qualitative measures, decision makers and refugee assistance organizations will be able to better assist Syrians who are integrating into Jordanian communities. Additionally, the research serves to show where Jordanian communities can improve on societal infrastructure to better improve the livelihood for both Jordanians and Syrians because the Syrian crisis will have a prolonged effect.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".