“Let us define ourselves”: forced migrants’ use of multiple identities as a tactic for social navigation
Bibliographic record
Abstract
BACKGROUND: The article examines how and why multiple identities are altered, used and discarded by forced migrants. METHODS: The research is located in the constructivist paradigm. We used thematic analysis to analyse data gathered through interviews with nineteen forced migrants. RESULTS: We found that, though individual migrants can make deliberate choices about which identities to be associated with, they are constrained in the process by external socio-economic factors that lead them to adopt identities that are perceived to be advantageous to navigate the new social system. Moreover, the construction of forced migrants' identity includes significant contextuality, transactionality and situatedness. CONCLUSIONS: Our research contributes to the literature on migrant identity practice concerning the stigma associated with forced migrant status and the extent to which migrants appraise their reception in exile as undignified. Additionally, examining migrant identities allows the researchers to apprehend the diverse facets of identity as far as migrants are concerned. Future research may draw a larger sample to examine other impactful dimensions of identity fluctuation, e.g. gender, education, social media, the extent of prior trauma, etc.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".