Demographic and socio- economic characteristics of Muslim women in Australia
Bibliographic record
Abstract
Description and rationale: Australia, accompanied by the United Stated and Canada as countries with long tradition of immigration, is best-known as a culturally divers country in the world. This fundamental characteristic has brought together Muslim populations like other various religious and ethnic backgrounds people from all corners of the world in Australia. It also allowed that despite some considerable swings over time mainly caused by adapting different immigration policies, Muslim populations experienced an increasing general trend over time so that it reached from 22311 in 1971 to 277967 in 2001. It is also worthy to add that Muslim population was the largest non-Christian religious group until 1991 and the second highest one in the census 2001 in Australia. Method and data: Secondary data analysis based on the Australian Census of Population gained from the Australian Bureau of Statistics (ABS) are used to examine the demographic and socio-economic characteristics of Muslim women aged 15-54 years old in 2001. In a comparative approach, they will also be examined with their Non-Muslim counterparts in terms of these characteristics. Expected Findings: The characteristics that are going to be discussed include population issues, education levels, English language proficiency, family formation, individual income, partner's income, duration of residence in Australia, the country of origin as well as the main initial results on employment status, occupation status, industries of employment, hours worked, and place worked.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.015 | 0.003 |
| Science and technology studies | 0.000 | 0.007 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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; both teacher heads agree on what is shown here.
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".