MAJOR PROBLEMS OF INDIAN EXPATS: AIMING WORK HARM REDUCTION FOR THEIR SUSTAINABILITY
Bibliographic record
Abstract
This study is conducted on the Indian expatriates working abroad. As the non supportive environment can cause major problems for them and also to their family who undergoes some challenges such as issues related to loneliness & adaptation & their Children may suffer some issues and not adapt well to their new school context. In the developed countries like CANADA, UK & USA where Indian community are nothing more than Native American or hardly behave like Indians resulting they are not coping with the new expats. Individuals from different cultures may encounter difficulties in comprehending each other's values and behaviour towards another leading Cultural clash. Everything has its cause and effect, one cannot exist without the other and this law exists through everything in nature. Work life harmony is also matters much than just work life balance. One should consider love, career and being happy for creating & maintaining harmony and balance in life. Our primary focus should be on our relationships with preferably the Nature, Society, Family & Friends then succeeding to the working environment. Understanding what we need in order to fulfil our requirements of happiness, financial security, relationship, friendship, passion, valuing & respecting, spending time with family, being honest with the friends, self caring.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".