Transnational sensemaking narratives of highly skilled Canadian immigrants' career change
Bibliographic record
Abstract
Purpose The authors answer calls for research on the experiences of international professionals' career transitions by investigating how highly skilled immigrants make sense of their career changes in the host country's labor market. Design/methodology/approach The authors report on a qualitative, inductive and elaborative study, drawing on sensemaking theories and career transitions literature and nine semi-structured reflective interviews with highly skilled Canadian immigrants. Findings The authors identified four career change narratives: mourning the past, accepting the present, recreating the past and starting fresh. These narratives are made sense of in a transnational context: participants contended with tensions between past, present and future careers and between relevant home and host country factors affecting their career decisions. Participants who were mourning the past or recreating the past identified more strongly with their home country professions and struggled to find resources in Canada. In accepting the present and starting fresh, participants leveraged host country networks to find career opportunities and establish themselves and their families in the new environment. Originality/value A transnational ontology emphasizes that immigrants' lives are multifaceted and span multiple national contexts. The authors highlight how the tensions between the home and host country career contexts shape immigrants' sensemaking narratives of their international career change. The authors encourage scholars and practitioners to take a transnational contextual approach (spatial and temporal) to guide immigrants' career transitions and integration into the new social environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".