Immigrant Intercultural Development: A Perspective Taking Approach to Support Intercultural and Workplace Adjustment
Bibliographic record
Abstract
This two-phased study sought to understand the challenges Canadian immigrants faced in their workplace and intercultural adjustment as technology mentors and explore intercultural development interventions to address them.Drawing on sixteen qualitative interviews with ten Canadian immigrants and two co-founders of their non-profit employer, I found that technology mentors faced communication, technology, and personal challenges and were concerned with their intercultural and workplace adjustment as well as long term work integration.Clean spinning, a non-directive coaching intervention based on clean language and emergent knowledge principles, was used to facilitate perspective taking and support their intercultural development towards addressing their adjustment challenges.I found that clean spinning supported participants in finding resource(s) to address their topic or reframe the way they understood their topic and their relationship to it.These positive outcomes suggest the need for alternative types of intercultural development that support perspective-taking and self-reflection.Acknowledgements I really would not have been able to accomplish this without the help of my supervisor Dr. Luciara Nardon.Her guidance, insights, mentorship, and coaching throughout my master's experience is one of the most, if not the most, invaluable experience that I gained throughout this program.Thank you for believing in me and my work, and for enabling me to engage in my own process of engaging with different perspectives.A big thank you to all the participants who agreed to be interviewed and the Canadian non-profit organization that allowed me to explore my thesis topic within their organization.I learnt so much from the participants and this experience.The organization was always incredibly supportive and open to discussing the varying aspects of this project.Without their help this project would not have been able to happen.I'd also like to thank Dr. Gerald Grant and Dr. Daniel Gulanowski for their support as part of my academic committee, the Sprott School of Business, and everyone who contributed to my Carleton University experience.Finally, a big thank you to my family and
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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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".