Australian Muslim Jobseekers and Social Capital
Bibliographic record
Abstract
This article uses the concept of social capital to analyse data about Muslim jobseekers attempting to enter the Australian labour market. They often relied on their own social networks to find work rather than maximize the support of employment service providers. The study demonstrated the range of Muslim jobseekers and their social networks in an atmosphere of fear and intolerance. Three categories of Muslim jobseekers – from advantaged to disadvantaged – were identified. It is concluded that the most disadvantaged Muslim jobseekers can benefit greatly from increased support offered by employment service providers. Cet article présente une analyse des gens de confession musulmane qui cherchent à entrer sur le marché du travail en Australie. Nous avons observé que ces gens comptaient souvent sur leurs propres réseaux sociaux pour chercher un emploi et que quelquefois ils ne voulaient pas utiliser les services d’aide. Nos recherches décrivent en détail les expériences de ces demandeurs d’emploi. Il semble qu’il y ait un climat de peur et d’intolérance dans la société australienne qui pousse ces personnes à compter sur leurs propres ressources et réseaux, qui sont nécessairement limités. Nous avons identifié trois catégories de demandeurs d’emploi musulmans: les demandeurs les plus favorisés, les demandeurs avec quelques avantages, et les demandeurs les plus défavorisés. Nos recherches indiquent que les musulmans qui ne réussissent pas à trouver un emploi sont ceux qui sont les plus défavorisés dans la société. Ces derniers devraient obtenir un meilleur soutien des services d’aide à la recherche d’un emploi quand ils cherchent à entrer sur le marché du travail en Australie.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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; 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".