The Impacts of Drug and Alcohol use on Sentencing for First Nations and Non-Indigenous Defendants
Bibliographic record
Abstract
This paper examines the ways personal use of illicit substances and alcohol are constructed as either mitigating or aggravating factors to explain offending. We consider the differential constructions of these factors for people who appear in supreme and district courts in northern Queensland, Australia, for offences involving illicit substance use, alcohol use, drug-related offences, and violence. Qualitative analysis of courtroom observations is understood through the lens of Critical Race Theory (CRT). Our findings reveal that personal use of illicit substances was primarily constructed by legal practitioners as an indicator of disadvantaged circumstances when discussing non-Indigenous defendants. In these cases, drug use was connected to other disadvantages such as poor mental health, physical pain, and trauma. In contrast, alcohol use was primarily raised as an aggravating factor for First Nations defendants, constructed by legal practitioners as a personal flaw linked to violent offending, and overshadowed the interrelated disadvantages that many First Nations defendants experience. This reflects social attitudes about First Nations people, reinforces individualistic explanations for offending patterns, and points to the institutional racism embedded in the structural processes of Queensland's higher courts that continues to profoundly impact First Nations people.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".