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Record W4380354066 · doi:10.5204/ijcjsd.2641

Driver Licences, Diversionary Programs and Transport Justice for First Nations Peoples in Australia

2023· article· en· W4380354066 on OpenAlexaboutno aff
Gina Masterton, Mark Brady, Natalie Watson-Brown, Teresa Senserrick, Kieran Tranter

Bibliographic record

VenueInternational Journal for Crime Justice and Social Democracy · 2023
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsnot available
Fundersnot available
KeywordsInjusticeEconomic JusticeCriminal justicePolitical scienceDisadvantageScope (computer science)State (computer science)Face (sociological concept)LawCriminologySociologyComputer science

Abstract

fetched live from OpenAlex

In Australia, one significant cause of the imprisonment and disadvantage of First Nations people relates to transport injustice. First Nations people face obstacles in becoming lawful road users, particularly in relation to acquiring driver licences, with driving unlicensed a common pathway into the criminal justice system. This paper identifies that while some programs focus on increasing driver licensing for First Nations people, there are significant limitations in terms of coverage and access. Further, very few diversionary or support programs proactively address the intersection between First Nations people’s driver licensing and the criminal justice system. Nevertheless, it is argued that scope does exist within some state and territory criminal justice programs to enhance transport justice by assisting First Nations people to secure driver licensing. This paper highlights the need for accessible, available and culturally safe driver licencing support programs in First Nations communities led by First Nations people.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.341
Threshold uncertainty score0.430

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.035
GPT teacher head0.308
Teacher spread0.273 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations3
Published2023
Admission routes1
Has abstractyes

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