Developing and testing a framework for understanding refugees' job search processes
Bibliographic record
Abstract
Purpose This article outlines the development of the Refugee Job Search Process Framework (RJSPF), which was created to help identify barriers that refugees face when trying to find employment. The framework incorporates an interdisciplinary, multi-level approach to the job search, delving into research from migration studies and Industrial/Organizational psychology to outline factors that exist on both the side of the refugee applicant and the organization at each stage of the RJSPF. The authors also tested the RJSPF with Syrian refugees and service providers in Canada to examine the validity of each component of the model. Design/methodology/approach The authors used a semi-structured format to interview refugees and service providers on their experiences in either trying to find employment or helping their refugee clients with the job search process. After transcribing the interviews, the data were independently coded, quantified, and analysed using Nvivo software to validate the RJSPF. Findings The majority of the RJSPF either had high or moderate support from the interviews. The authors also identified 6 broader themes using thematic analysis, which include language fluency, credential recognition, Canadian experience “catch 22”, cultural incongruencies, employer exploitation, and mental health for successful employment. Originality/value The RJSPF is a new integration of disparate theories of job search experiences in a literature that lacks an organizing framework and perspective on the unique challenges refugees face in this area compared to other newcomers. In doing so, the authors use an interdisciplinary, multi-level approach that extends the nomological network of barriers facing refugees, therefore informing future research and practice.
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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 | 0.031 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.011 | 0.022 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| 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".