Bibliographic record
Abstract
In Australia, Canada, New Zealand and the United States of America, Indigenous peoples continue to experience incarceration at markedly disproportionate rates. Some scholars have criticised criminology for contributing to this social problem by marginalising Indigenous peoples in research and research publications. This study is a first attempt to quantitatively evaluate the (de)colonised state of contemporary criminology. It involves a comprehensive review of research on 'Indigenous peoples in the criminal justice context', which has been undertaken in aforesaid countries and was published in elite criminology journals over the past decade (2001-2010). The findings reveal that publication rates on the subject are low both compared to incarceration rates and compared to the quantity of academic discourse about other disproportionately incarcerated social groups. Since an adequate, i.e. attention-grabbing, quantity of academic discourse has been linked to the public recognition of social problems, the dearth of publications on the subject suggests that mainstream criminology inhibits public attention to the issue and thus contributes to the marginalisation of Indigenous peoples, the reproduction of social inequality and the preservation of elite power.
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 | 0.033 | 0.123 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".