A positive shift : Chinese post-secondary students who identify as thriving discuss their cross-cultural transition experiences in Canada
Bibliographic record
Abstract
Throughout the literature, international students are portrayed as a vulnerable population because there is a tendency towards focusing solely on the challenges they encounter while studying abroad. As a result international students appear to be perceived as being prone to develop psychological distress. Inter-cultural variations also appear less pronounced because international students are often studied as a whole group, and results from various studies combine several culture-of-origins. Consequently, few studies have focused solely on understanding the overall transition experience of a selected population of international students with attention awarded to areas beyond challenges, such as the positive experiences, their desires, and unique characteristics. This study, therefore, sought to determine the different facets of the cross-cultural experiences for a specific group of students: Chinese international students from China. Both the positive and negative qualities of the transition were assessed. Furthermore, in order to portray the capacity for international students to prosper while studying abroad, only students who expressed they were “doing well” regarding the cross-cultural changes were selected. The methodology employed was the Enhanced Critical Incident Technique (ECIT), and this method identified 135 helping incidents, 102 hindering incidents, and 36 wish-list items. The helping incidents were grouped into 9 facilitating categories, the challenging incidents were grouped into 7 hindering categories, alongside 1 sub-category, and the 36 wish-list items were grouped into 3 categories (with 1 sub-category). It became evident in this study that by addressing a unique international student population, there appeared to be greater awareness towards the distinct experiences of this population, as well as the opportunity to contribute to the growing literature on international students. More importantly, the study provided a holistic perspective of international students by attending to both facilitating and hindering events. Supported by the deliberate selection of students who report that they are adapting well, the findings seemed to further corroborate a growing trend in the literature that views international students as capable of thriving and excelling, regardless of the difficulties or challenges they may encounter during their cross-cultural transition.
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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 | 0.010 | 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) | 0.004 | 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".