Internet, Social Media, and Settlement: A Study on Bangladeshi Immigrants in Canada / Internet, médias sociaux, et établissement : une étude des immigrants bangladais au Canada
Bibliographic record
Abstract
This study describes Internet and social media usage among Bangladeshi immigrants in Ontario, Canada. Using a mixed-method approach, the study conducted semi-structured interviews with 60 Bangladeshi immigrants in Ontario and gathered 205 completed survey responses. The findings show that recent Bangladeshi immigrants in Canada significantly depend on the Internet and social media tools in preand post-arrival contexts. Specifically, the findings indicate that the use of ethnic community social media forums among the Bangladeshi community are used for help with various aspects of their settlement into Canadian society, including learning about life in Canada, accommodation, and employment. The findings also reveal the important role that social media networking tools like LinkedIn play in recent newcomers’ employment-related decision making and settlement in Canada. The author calls for further studies on immigrants’ use of the Internet and social media—in particular, for studies on ethnic community social media forums and their role in newcomers’ settlement. The author also urges countries welcoming immigrants across the globe such as Australia, Canada, New Zealand, the United States, and the United Kingdom to develop timely, need-based, online services for skilled immigrants and their dependents in order to meet the diverse needs of highly skilled immigrant populations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.017 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".