Narratives of Intercultural and Holistic Transformative Learning Experiences of Professional Immigrants: Expanding Learning through Relationships
Bibliographic record
Abstract
Colombians with university degrees still struggle to find their professional place and quality of life in their home country (country of origin). Therefore, many professional Colombians (individuals with a university degree) immigrate to Canada to not only succeed professionally, but also to improve their quality of life. However, many immigrants discover that having professional experience and credentials from their home country and speaking the target language are not enough to facilitate quick entry into the job market, within their professional field, and to integrate into their host communities so they could feel professionally successful. According to Armony’s (2014) quantitative study, Colombian immigrants have experienced the most discrimination, unemployment, and poverty of any Latin American cultural group of immigrants in Canada. The purpose of this qualitative study with narrative inquiry methodology was to explore the professional pathways of eight professional Colombian immigrants who felt successful in Canada and had two or more years of adaptation and integration to answer the following question: To what extent did professional Colombian immigrants experience holistic Transformative Learning (TL) and enhance Intercultural Communicative Competence (ICC) after living in Canada for two or more years, on their path to professional success? The participants implemented various learning strategies that cultivated interpersonal connections that fostered changes in their frames of reference; they became more open-minded, flexible, resilient, and humble, experiencing various levels of holistic TL while enhancing their ICC.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".