Communication Issues Faced by Indian Undergraduate Students in Canada
Bibliographic record
Abstract
The ability to communicate in a foreign country has been troubling the students for a long time. According to a study done by Gebregergis (2018), one of the major contributing factors to depression among international students was a lack of confidence to communicate in the local language. Additionally, difficulties with English as a language to communicate was a big challenge in adapting to a new academic environment (Kristiana et al., 2022). In India, even though students have a choice to study in colleges with international collaboration, they still aspire to pursue higher education abroad (Gopinath, 2015) due to scholarship, country image, program structure, and social media (Kazako et al., 2017). According to the Indian Ministry of External Affairs, the total number of Indian students enrolled in undergraduate courses across 79 countries during 2022 was 13,24,954 and Canada was second highest with 183,310 students (India, 2023).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.013 |
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; both teacher heads agree on what is shown here.
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".