Global Ambitions, Local Support. Understanding Faculty Influence on International Students' Career Development
Bibliographic record
Abstract
International students’ education and migration journeys have become more prominent in public discourse, often with stereotypes of students as low-wage workers rather than emerging professionals. When their own narratives are centred, however, international students emphasize their hopes for faculty and institutional support in reaching their career aspirations. These aspirations may also be linked to post-graduate work permits or migration plans. A student-centred faculty intercultural teaching taxonomy developed at a Western Canadian postsecondary institution highlights five faculty practices that support career development: (1) affirming students’ skills, knowledge, and experience; (2) recognizing non-academic factors, including present work, in students’ lives; (3) supporting transition to the labour market; (4) building connections with students; and (5) demonstrating cultural sensitivity. This web of practices highlights the faculty career influencer function. As career influencers, faculty can recognize the systemic barriers students face in their education and immigration journeys, guide students in questioning dominant narratives, provide support and advocacy in pursuing meaningful professional career paths, and serve as advocates. International students identify faculty as key career influencers. Institutional support and faculty development are required help faculty take a holistic view of students’ career journeys and recognize their critical role as influencers, guides, and advocates.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".