The longitudinal adaptation process of international students in Canada
Bibliographic record
Abstract
In the present chapter an acculturation model referred to as the Multi-dimensional Individual Difference Acculturation (MIDA) model has been examined using longitudinal data. The present data are the first to capture the directional paths between the predictor and outcome variables in the MIDA model. Sixty international students living in Canada participated in online surveys at two times. The results of the study indicated that resources and difficulties (Psychosocial Resources, Co-National Connectedness, and Academic Hassles) at Time 1 (T1) were better predictors of health status and socio-cultural adaptation (Ingroup Contact, Outgroup Contact, and Psychophysical Distress) at Time 2 (T2, 18 months later) than the reverse model. These findings provide strong support for the MIDA model and demonstrate that the predictor variables do, in fact, predict the outcome variables over time and, more importantly, that the reverse is not the case.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".