In-Class and Out-of-Class Experiences of International Graduate Students in the United States
Bibliographic record
Abstract
This qualitative case study aims to understand participants’ perceptions of In-Class and Out-of-Class experiences of graduate students in the United States. Data were collected as part of a larger mixed-methods study involving 110 participants identified by the institution’s Center for International Education. The participants consisted of 12 graduate students enrolled in doctoral degree programs in the Rocky Mountain region of the United States who represented the following countries: Thailand, Saudi Arabia, Norway, Mexico, and China. Data were collected through semi-structured interviews and coded using consensual qualitative research methodology (Hill, et al. , 2005). To highlight our findings and ensure the privacy of our participants, we created three firstperson composite portraits (Rossman & Rallis, 2003). Common themes are presented with the participants’ rich descriptions. Implications and directions for future research are discussed. Key words: International students; Challenges; Benefits; Recruitment; Qualitative
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".