Variables associated with the employment and occupational status of Southeast Asian women refugees
Bibliographic record
Abstract
There have been numerous studies on the economic, social, and emotional lives of the Southeast Asian refugees (Adelman, 1982; Beiser, Johnson, & Roshi, 1994; Haines, 1989; Neuwirth, 1984; Nutter, 1984; Whitmore, Trautmann, & Caplan, 1989), yet the lack o f focus on Southeast Asian women refugees is prevalent. Human capital theory explored Southeast Asian women refugees' unique settlement experience as workers in Canada. Data from the Refugee Resettlement Study, " A Ten Year Study of Southeast Asian Refugees in Canada" (Beiser et al., 1994) was used to examine the employment experiences of women from Vietnam and Laos who came to Canada as refugees. The majority of the women were employed and were in jobs with low occupational status. Logistic regression was used to analyse variables associated with employment and occupational status. High English language proficiency was associated with being employed and having high occupational status. In addition, being younger increased employability. When the individual items within the English language proficiency scale were assessed, ability to read English and low ethnic concentration promoted employability. Ability to write English and low ethnic concentration increased the likelihood of having higher occupational status. This study contributes to the limited literature on refugee women's human capital accumulation and employment experiences during settlement in the host country.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".