A Narrative Inquiry into the Experiences of Racialized Internationally Trained Professionals in the Academe: A Pedagogy of Cultural Wealth
Bibliographic record
Abstract
Though significant attention has been drawn to the diversity or lack thereof within the Canadian academe, the attention shown to professionals in non-faculty positions has been quite insignificant in comparison to that shown to faculty members. The purpose of this study is to investigate the experiences of racialized internationally trained professionals in academe and to raise awareness of the gap created by a lack of research into the experiences of non-faculty staff members in Canadian academe. In this study, a narrative approach to understanding the experiences of racialized internationally trained professionals (both faculty and non-faculty) in Canadian post-secondary institutions was used to investigate how these professionals respond to and deal with real and perceived challenges, and how these experiences affect them both professionally and personally. This study includes six participants with professional experience ranging from two to twelve years, and their experiences are situated within a pedagogy of cultural wealth. This study identified a lack of support, foreign credential recognition, institutional structure and responsibilities, and language and cultural differences as common themes in participant narratives. Implications for faculty, non-faculty, post-secondary institutions, and future research are also presented.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.031 | 0.022 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".