Building Career Pathways for Resettled Refugees in the United States
Bibliographic record
Abstract
The purpose of this paper is to explore career pathways for refugees who have been resettled in third countries. A career pathway for a refugee means, how likely is it that one can find self- sustaining wages and/or a fulfilling profession in their country of resettlement, and how is that process supported by the third country. \n \nThis paper explores the aim of answering three questions: How is the United States currently addressing career pathways for resettled refugees? What career pathway innovations are happening in other countries? What recommendations could possibly produce better outcomes in the United States? First, it outlines what career pathways are, why they are important, and what are the barriers, through a literature review and secondary sources. Subsequently, it explores how the United States is currently addressing career pathways for resettled refugees, through a literature review and secondary sources. Next, in light of the current situation with Ukraine, it discusses a case study of career pathways of Soviet refugees in the United States during the 1990s, completed by surveying refugees who arrived during that time between the ages of 30-50. Then, it looks at career pathway innovations being employed in Canada and Sweden, through a literature review and secondary sources. Lastly, it offers recommendations that could possibly produce better outcomes in the United States, including recommendations for policies, programs, and businesses. \n \nThe objective of the paper is to provide recommendations to support third countries to address these issues in myriad ways, in order for refugees to move out of survival jobs and into careers with more sustainable wages, consistent schedules, and ample benefits; which will allow them to feel fulfilled while also contributing to their new homes, communities, and economies.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".