Identity work in refugee workforce integration: The role of newcomer support organizations
Bibliographic record
Abstract
How does professional employment support provided by newcomer support organizations (NSOs) influence highly-skilled refugees’ professional identities and workforce integration? To answer this question, we draw on interviews with 25 managers and staff of NSOs in Canada and 11 recently arrived, highly-skilled refugees. We contribute to the literature on refugee workforce integration by shedding light on the dynamic process of employment support in which NSOs engage in sensegiving practices and influence refugees’ understanding of career options, assessment of opportunities, and their professional identity responses. We found that NSOs attempted to manage refugees’ expectations of career opportunities while fostering hope for the future and that refugees reacted to NSOs’ sensegiving practices by resisting expectation management messages, recrafting a new identity, or bracketing the present as transitory. We highlight the role of external agents in sensemaking and identity work by exploring work role transitions caused by forced migration. Furthermore, we uncover the dynamics of power and contextual constraints that influence sensegiving interactions. From a practical point of view, we argue that in the absence of quality employment opportunities, the reliance on refugees’ resilience and their motivation for long-term professional integration may further marginalize them.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.013 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".