Unveiling the<i>Canvas Ceiling</i>: A Multidisciplinary Literature Review of Refugee Employment and Workforce Integration
Bibliographic record
Abstract
Abstract Increasing levels of displacement and the need to integrate refugees in the workforce pose new challenges to organizations and societies. Extant research on refugee employment and workforce integration currently resides across various disconnected disciplines, posing a significant challenge for management scholars to contribute to timely and relevant solutions. In this paper, we endeavour to address this challenge by reviewing and synthesizing multidisciplinary literature on refugee employment and workforce integration. Using a relational framework, we organize our findings around three levels of analysis – institutional, organizational and individual – to outline the complexity of factors affecting refugees’ employment outcomes. Based on our analysis, we introduce and elaborate on the phenomenon of thecanvas ceiling‒ a systemic, multilevel barrier to refugee workforce integration and professional advancement. The primary contributions of this paper are twofold. First, we map and integrate the multidisciplinary findings on the challenges of refugee workforce integration. Second, we provide management scholarship with a future research agenda to address the knowledge gap identified in this review and advance practical developments in this domain.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".