The Nuanced Footprints of Covid-19 Predicament on Labour Market Integration of Migrants in Finland
Bibliographic record
Abstract
The COVID-19 pandemic has left its unprecedented footprints and aftershocks on every aspect of human endeavour, including labour on the move. Against this backdrop, this study aims at providing an extensive comprehension of the footprints of COVID-19 predicament on migrants, refugees and asylum seekers’ (MRAs) lives, work and labour market incorporation experiences in Finland. The study adopted the subtle biographic narrative interviews with MRAs. The findings, though mixed, reveal deepening inequality and insecurity in the labour market of Finland for migrants. Conversely, self-learning, virtual learning, manifestation of hidden talents, development of new hobbies and transferable skills, as well as new healthier lifestyles for MRAs were apparent in the findings. The analysis of the study can serve as both an authentic empirical knowledge to guide migrants’ populations and a reference source for academic purposes; the latter can negotiate the impact of disruptive events and sudden crisis on migrant populations, whose unique circumstances and characteristics require inclusive policies and strategies. The study made original and valuable insights into the impact of the COVID-19 pandemic on migrants as a significant group in the labour market architecture, however, vulnerable one in terms of precarious work, implying that pandemics and similar eventualities call for (re)designing considerable support systems and extending them to MRAs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".