Perspective Of Indian Immigrant Students Going Abroad
Bibliographic record
Abstract
It is undoubtedly not a simple and easy decision to leave one's comfort zone in your home country and move to a completely new host country, in search of better education and more career prospects. Also, globalization has created a kind of scenario across the world that the experience and exposure fascinate the students to take this tough decision of leaving their home country. In Indian context, there is a significant transformation in the education industry during the last decade as a large number of Indian students are moving to foreign destinations every year not only for earning international degrees but also building careers in diverse fields. Present study describes about the challenges faced by Indian students when they travel to the host countries USA, Australia, and Canada through an empirical mode. The primary data is collected from a sample of four hundred Indian Immigrant students of USA, Australia and Canada based on questionnaire through snowball sampling method of data collection. The objectives included to analyse perception of Indian immigrant students regarding issues and challenges on gender basis and to analyse perception of Indian immigrant students regarding issues and challenges on the basis of country. The findings indicated host country language, inconvenient travelling, unpleasant behaviour of people as more challenging for females as compared to males in the host countries. Also, understanding of host country language, costly medical facilities, missing the country food crazily are more challenging for Indian immigrant students in Canada.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".