An Economic Analysis of Indian Emigrants in Saudi Arabia during COVID-19 Pandemic
Bibliographic record
Abstract
The current pandemic of Covid-19 has not only changed the life style of billions of persons in the world but also severely disturbed their livelihood. Travel ban and business restrictions has frozen the movement of people, changed the occupational status and consumption pattern of people in almost every country. India is leading country to supply labour ( around 18 million ) in the world and top remittances receiving country globally from 2008 to 2020-21 and Saudi Arabia is third largest remittances source country in the world. The oil boom of 1970s in the Gulf countries increased the demand for unskilled and semiskilled labour. Majority of the skilled or semi-skilled labour were supplied to the Gulf countries from southern state of India like Kerala or Tamil Nadu and unskilled or semi-skilled labour had been supplied from northern states of India like Uttar Pradesh and Bihar. The migrants or the refugees in any society agonized the most during any pandemic, hence it is essential to analyze the economic impact of Indian emigrants in Saudi Arabia during the Coronavirus disease. This study is quantitative in nature and based on both primary and secondary data. The sample of 100 unskilled or semi-skilled labour were collected through a structured questionnaire. 60 samples of migrants from Uttar Pradesh and 40 samples of migrants from Bihar were collected through multi-stage sampling technique in the month of March-April 2021. The study has confirmed that remittances and earnings of the migrants had been negatively affected during COVID-19. The loss of earnings and spread of Coronavirus in their native place had a severe mental impact on the migrants. Chi square test result confirms that there is a significant difference of feeling nervous, depress and lonely across the different states of origin of the migrants.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".