Bayesian Analysis of the Sudanese Immigrant Youth Crime Rates and the “Likelihood” of Committing Violent Offence than an Australian-Born
Bibliographic record
Abstract
This paper presents a mixed method of inquiry into most of the public notions, shaping the Sudanese immigrant community’s perception Australia wide. Firstly, a qualitative review regarding two remarks by Australian public figures will be considered and analyzed; and secondly, Bayesian analysis (BA) will be considered to analyze the randomness of the crimes: BA, is a highly predictive methodological tool used in a wide range of applications. For example, in predicting of crimes based on prior occurrences of an offence or groups of offences. Thus, the Bayesian analysis considers the hypothesized relationship between ‘Ethnicity and Criminality’; the emphasis is on the recorded Crime figures involving immigrant youth of the Sudanese-born residing in the state of Victoria. The figures are drawn mainly from the Australian statistical agencies and media sources; the Australian Bureau of Statistics (ABS), media reports, community’s prison population (CPP) and the overall Sudanese immigrant population in Australia (SIPA); comparative considerations with the overall Australian population (APP) and the Australian-born prison population (ABPP) from the years 2006 to 2007 will be looked into. The study concludes by suggesting the policy implication of this findings and future research directions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".