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Record W3126406985 · doi:10.3390/socsci10020068

Racial Profiling, Surveillance and Over-Policing: The Over-Incarceration of Young First Nations Males in Australia

2021· article· en· W3126406985 on OpenAlexaboutno aff
Grace O’Brien

Bibliographic record

VenueSocial Sciences · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Discipline and Inequality
Canadian institutionsnot available
Fundersnot available
KeywordsJuvenileCriminologyCriminal justiceProfiling (computer programming)Juvenile delinquencyEconomic JusticeRacial profilingPolitical scienceSociologyLawGender studiesRace (biology)

Abstract

fetched live from OpenAlex

Historically, countries such as Australia, Canada and New Zealand have witnessed an increased over-representation of minority groups who are exposed to the criminal justice system. For many years in Australia, young First Nations males have been over-represented in the juvenile justice system in all states and territories. Many of these young males have disengaged from their schooling early, some through deliberate exclusion from the education system and others by choice. However, the choices for many young First Nations males may not be as clear cut as first might seem. This paper shows that over-representation in the juvenile justice system may be as a direct result of racial profiling, surveillance and over-policing of First Nations peoples within Australia. The literature addresses the ways in which young First Nations males experience these phenomena from an early age, and the long-term effects and consequences that can arise from these occurrences. An analysis of the current research both internationally and within Australia is thus conducted.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score0.251

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.094
GPT teacher head0.434
Teacher spread0.341 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations22
Published2021
Admission routes1
Has abstractyes

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