Understanding the needs and experiences of Asian Female International graduate students
Bibliographic record
Abstract
The uniqueness of being female, international, and a graduate student poses its own challenges that warrant special attention and efforts from institutions and their leaders. Previous studies have addressed issues and challenges faced by international students, including financial stress, homesickness, racism, discrimination, barriers to care, and mental health issues such as depression and anxiety (Anandavalli et al., 2021; Kawamoto et al., 2017; King et al., 2023; Lee and Rice, 2007). However, research on understanding these students’ experiences must take into account the local context, as well as challenges specific to intersectional identities. This understanding can then inform programming designed to meet the needs of this group. The current study used a multiple case study design to examine Asian female international graduate students’ overall experiences, needs, challenges, and barriers to accessing relevant resources, programs, and services. Body mapping exercises and semi-structured interviews were conducted with 10 participants. Body mapping is the process of creating a life size human body image using art-based techniques such as painting and drawing to represent aspects of people’s lives and the worlds they live in. Data collected from the body mapping exercises and semi structured interviews were analyzed using reflective thematic analysis, and themes and codes were identified. Data were organized by major themes discussed, most recurrent themes followed by sub themes which were supported by direct quotes from participants. Body maps were analyzed individually, focusing on descriptive details as well as researcher understanding from interviews and narratives provided by participants. The study identified challenges and provided recommendations to enhance existing programs and services as well as preventative measures that can address the unique needs of this group.Keywords: intersectionality, higher education, resources, well-being
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".