Migration-related Factors and Settlement Service Literacy: Findings from the Multi-site Migrants’ Settlement Study
Bibliographic record
Abstract
Migrants' access and effective utilisation of settlement services depend on their level of settlement service literacy (SSL). However, SSL is multi-dimensional in nature and has many facets that are influenced by demographic and migration-related factors. Identifying factors that drive various components of SSL, and thus allowing for more focused development of specific dimensions, is critical. The aim of this study was to examine the relationship between components of SSL and migration-related and migrants' demographic factors. Using a snowball sampling approach, trained multilingual research assistants collected data on 653 participants. Data were collected using face-to-face or online (phone and via video platforms such as Zoom and Skype) surveys. Our findings suggest that demographic and migration-related factors explained 32% of the variance in overall SSL; and 17%, 23%, 44%, 8%, 10% of the variance in knowledge, empowerment, competence, community influence, and political components of SSL respectively. SSL was positively associated with pre-migration and post-migration educational attainment, being employed in Australia, being a refugee, coming from the sub-Saharan region but negatively associated with age and coming from the East Asia and Pacific region. Across SSL dimensions, post-migration education was the only factor positively associated with the overall SSL and all SSL dimensions (except the political dimension). Employment status in Australia was also positively associated with competency and empowerment, but not other dimensions. Affiliating with a religion other than Christianity or Islam was negatively associated with knowledge and empowerment whilst being a refugee was positively associated with knowledge. Age was negatively associated with the empowerment and competency dimensions. The study provides evidence of the importance of some pre- and post-migration factors that can assist in developing targeted initiatives to enhance migrants' SSL. Identifying factors that drive various components of SSL will allow for more focused development of specific dimensions and therefore is critical.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".