Shakamohtaa: Connecting and Coming Together to Support International Student Career Readiness
Bibliographic record
Abstract
Abstract\nIn the evolving Canadian landscape, permanent residency acquisition has undergone a transformative shift from land sales to educational credential procurement. Canadian higher education markets post-secondary qualifications to international students (IS) seeking migration routes, posing nuanced challenges. IS, despite holding higher education credentials, often find themselves relegated to non-field specific jobs due to existing disparities in the Canadian job market. Amid this equation, IS grapple with the essential need for pre-and-post graduate career experiences to fulfill eligibility criteria for permanent residency application. This pursuit extends beyond merely aligning with their credentials, requiring conformity to approved national occupation codes aligned with Canadian skilled labor objectives. This Organizational Improvement Plan explores factors hindering IS’ pre-and-post graduate career readiness. Leveraging the Michif term “Shakamohtaa” to emphasize community collaboration this study employs critical, transformative, and adaptive leadership approaches, the Change Path Model, and evaluative strategies such as appreciative and PDSA inquiry cycles, and the DICE Framework to incorporate key performance measures. Focused on a specific institution in rural Ontario, this investigation explores effective measures for bureaucratic institutions to transform and adapt IS career readiness approaches through adopting a culturally responsive lens to address disparities in IS career readiness, preparedness, and post-graduate employment. This study seeks to unveil a pervasive problem of practice that exists across the Canadian higher education sector and aims to develop more effective career support for IS graduating from diverse faculties.\nKeywords: international students, career experience, internationalization, immigration, transformative leadership, adaptive leadership, change path model, EDID
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".