Making sense of immigrant work integration in Canada
Bibliographic record
Abstract
For immigrants arriving in a new country, the excitement for a new beginning can be overshadowed by the multiple anxieties that accompany integrating into sometimes vastly different living and working environments and cultures.This is especially true for highly qualified immigrants (QIs) -generally highly skilled immigrants with a foreign post-secondary education who relocate to another country to work and live.It would be fair to assume that countries looking to boost their skilled workforce would embrace such talent.However, for QIs, this is often not the case, and it could boil down to the complexities of sensemaking.Two Canadian scholars from diverse, but associated, fields of study have published research that shines a light on sensemaking amongst QIs and the multiple stakeholders they interact with.Their research could transform our understanding of the challenges facing immigrants.Drs Luciara Nardon and Amrita Hari from
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 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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.043 | 0.011 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".