The Auto-ethnographic Inquiry of a Female Chinese Graduate Student in Canada: Challenging, Accepting, and Transforming
Bibliographic record
Abstract
China remains the top country of citizenship for international students and female students (married and single) comprise part of Chinese international students. However, female international students as a marginalized group face multiple challenges and parental, marital, personal, and cross-cultural situational barriers. Relying on an auto-ethnographic inquiry, this study digs into my experiences and stories as a Chinese female graduate student in Canada to examine and connect with the academic climate and broader communities. Specifically, the gender-based, culture-based, and race-based challenges that I faced during my studies at a Canadian university including my coping strategies are explored. The constructivist paradigm as a theoretical framework is used to delve into my perceptions and understanding of my lived experiences. Data was collected from my journals, memories, and emails written during my study in Canada. Based on my experience, this study unveils motivation, knowledge, and organizational gaps faced by the female group because of gender inequality, and cross-cultural and cross-racial differences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".