Female Punjab International Students' Perceptions of Safer Acclimatisation to a Southern Ontario, Canada College: A mixed methods study
Bibliographic record
Abstract
This thesis examines the perceptions of female Punjab, India international students' safer acclimatisation to the new culture and systems at a Southern Ontario College. It uses their recommendations to create a safer future. Adapting to a new culture significantly negatively impacts students' ability to focus on learning in the classroom. While orientation is provided for the adjustment to the learning, the acclimatisation to a new culture and external systems is neglected. The legal system, banking, transportation, housing, and health care require attention for the students' safer adjustment outside the classroom. A conceptual framework was developed from the literature. Safety climate and Feminist theory are the to focus and guide the research. A mixed-methods approach of quantitative and qualitative methods is utilised. The data sources included 17 online survey responses and 11 one-to-one telephone interviews. The findings suggest that housing was the most considerable safety concern. The safety ranking was followed by safely navigating banking and employment, adjusting to the Canadian laws and consequences of the law, transportation, and health care. An emerging factor included trust and mistreatment by others from their community. The students' perception was that previous generations of their community, who immigrated to Canada, lacked awareness of Punjab society's current and progressive nature in Punjab. It is recommended that students have pre-arrival and post-arrival orientations in the presence of influential and trusted community leaders
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".