To stay or to leave? : an assessment of the social, economic, and political factors that influence international students when deciding to remain in, or leave Nova Scotia, upon graduation
Bibliographic record
Abstract
To stay or to leave?An assessment of the social, economic, and political factors that influence international students when deciding to remain in, or leave Nova Scotia, upon graduation By: Jodi-Ann Aera Francis-WalkerThis research examines critical factors graduating/recently graduated international students from Halifax Regional Municipality (HRM) universities encounter when deciding to remain in Nova Scotia, move to other provinces, or return home.The 2014 "Now or Never" report identified high rates of educated international students leaving Nova Scotia.This research uses Lee's Push-Pull Theory of Migration to fill the gap regarding the factors that influence this phenomenon.An online survey (94 respondents) and a focus group were utilized to gather information on social and economic situations, and experiences from a mixed group of international students.The results indicate that they face a series of push factors encouraging their departure from Nova Scotia, and pull factors encouraging them to stay.In both instances, factors are related to social, political, and/or economic reasons.The research highlights that these push-pull factors are not static, and will aid in understanding how political actors can move forward.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".