Exploring leadership skills: Indigenous students and study abroad – how universities can improve study abroad for Indigenous students
Bibliographic record
Abstract
Indigenization and internationalization have been at the forefront of post-secondary education for the last decade. As an inherent intersection, Indigenous student participation in study abroad offers opportunity to meld the two priorities. However, Indigenous student participation in study abroad has been significantly less than non-Indigenous student participants. One element that would have significant influence on increasing student engagement and success in this fundamental area is an effective and inclusive Indigenous student study abroad success framework. The purpose of this dissertation was to collect narratives from Indigenous student participants in study abroad to contribute to the development of an Indigenous student study abroad success framework through a lens of leadership. Questions that guided this study concentrated on understandings of leadership gained through study abroad and how to improve the practices that support study abroad for Indigenous students. An Indigenous research approach guided semi-structured interviews with 5 Indigenous University of Saskatchewan students who have participated in a study abroad experience within the last four years (48 months) and 2 professionals working in the realm of study abroad. Inductive coding enabled themes to emerge from the data and honored the contributions and experiences of participants. Three main themes emerged from the narratives shared: support, program structure, and leadership. With intuitive intersections, the themes highlighted the experiences of students and potential for strengthening study abroad programming for Indigenous student participants. The findings from this research will contribute to improving support for study abroad for Indigenous students at the institution, nationally, and internationally. Furthermore, the goals of this research are to enhance the immense intercultural learnings experienced through study abroad for Indigenous students as future leaders and contribute to a significant gap in literature on the subject. The implications of this research extend to improving practice in educational leadership by equipping professionals with a framework and understanding to better support Indigenous students in study abroad with the potential for positive impact reaching beyond professionals and student participants to communities and inspire inter-generational change.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".