Learning by doing migration: temporal dimensions of life course transitions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it