Learning by doing migration: temporal dimensions of life course transitions
Bibliographic record
Abstract
The increasing speed of societal, environmental, technological, and workplace changes brings into sharper focus the question of how people shape and learn from transitions, such as so-called ‘skilled migration'. Taking a doing transitions and doing migration perspective, I assert that transitions and migration do not simply exist but are constituted relationally through social practices and accompanied by learning processes. This paper reports findings from qualitative research into the question of how people learn and transform their understandings of (life)time when moving to a new country and seeking entry into the labour market. The study used the documentary method to analyse data from 20 biographical-narrative interviews with people who moved to Canada as adults. Findings indicate different modes of dealing with shifts in temporal contexts during migration as decompressing lifetime, losing time, and going with the flow. These modes are associated with positive transformative learning, negative transformative learning, and learning through participation in practices. This study has implications for theorising learning during life course transitions as a socially embedded process. It also points to the need for differentiated support as individuals seek to enter new labour markets or make career changes in the context of migration.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".