Recent African Refugees to Australia: Analysis of Current Refugee Services, a Case Study from Western Australia
Bibliographic record
Abstract
In the last decade the number of African refugees arriving in Australia has increased significantly, to the extent to which by 2008 they outnumbered all other refugee and humanitarian entrants to Australia (for example, in 2004-2005 75% of all refugee and humanitarian entrants to Australia were from Africa). Existing service provision models have been found to be ill-equipped to cope with this sudden influx and have struggled to cope with the unique needs of African refugees (trauma, cultural needs, racism and longer settlement adjustment periods – compared to other groups) in particular. This paper is based on a data-base and literature analysis of the numbers, issues and problems faced by refugees in Western Australia. Its major aim is to provide researchers and policy-makers with a resource base from which they can further their understandings of the plight of refugees in developing nations. As such much of the paper is based on analysis of a large amount of literature and data from government agencies, designed to provide an exhaustive overview of refugees, their experiences and gaps in service provision in Western Australia.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".