Bibliographic record
Abstract
This chapter discusses the concept of ‘access to justice’ with regards to rural victims of crime and argues that it may be the most important issue for theory and research in rural criminology over the coming decades. It begins by clarifying definitions of rural, victimisation, crime, harm, access and justice. Through various examples, it illustrates what access to justice means. Working from the concept of deservingness, it identifies two fundamental types of access to justice within which various examples can be categorised. The first access to justice issue is the lack of credibility and importance of rural peoples and rural communities; that is, the idea that police and other criminal justice services are less likely to be made available. Examples can range from the lack of police response to rural people as witnesses and victims to the uneven distribution of resources in favour of urban residents and policy-making related to safety and security that often ignores the rural. The second type is too much credibility, but this time as possible offenders/criminals, resulting in discriminatory enforcement by law enforcement and other inequities in the criminal justice system, as illustrated by the collective experiences of Indigenous peoples in settler societies like Australia, Canada, New Zealand and the United States.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".