A STUDY ON JOB SATISFACTION OF INDIAN EXPATS & THE IMPACT OF RUSSIA-UKRAINE WAR ON THEIR PROFESSION (WITH SPECIAL FOCUS ON PROFESSORS IN CANADA)
Bibliographic record
Abstract
Education plays an important role in Canada's development program. Canada has a number of Universities that are accessible to Canada and immigrant professors according to various national curricula. Employee job satisfaction is a key issue for any organization. The success of an organization depends solely on the qualitative and quantitative efforts of its employees. Dissatisfaction among employees increases absenteeism, leads to depression and negatively affects their work. Therefore, it is important to review the reasons for their dissatisfaction and analyze the reasons for their dissatisfaction.\n\nAlthough there are Colleges in Canada offering curriculum for working Indians, it is important to consider the quality of education as compared to Indian Colleges. The quality of education depends on the level of teaching of the Professors working in these Colleges. There are no specific rules regarding pay structure, qualification etc. Professor satisfaction is very important as dissatisfied Professors cannot pay attention to their responsibilities and may reduce the quality of education. This can have a detrimental effect on the future of many students learning. Therefore, an attempt has been made to study the job satisfaction level of Indian foreign Professors working in the state of Canada. This study focuses on Professors working in the Universities of UG & PG education.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".