Bibliographic record
Abstract
Worldwide and compared with urban population centres, state penal punishment sanctions have unique and distinct consequences for rural communities and people. The United Nations World Social Report 2021 establishes that extreme poverty, defined as living on less than $USD1.90 a day, is primarily a rural phenomenon. Internationally, four out of five people live in this condition, characterized by increased rates of socio-economic inequality, particularly in the wake of the spread of COVID-19. Even prior to this pandemic, a pattern of global rural spatial inequality was identified, linked to urbanization, technological innovation, climate change and out-migration. International research findings acknowledge a relationship between persistent poverty and increased rates of crime and punishment that impact the most vulnerable in these spaces – Indigenous people, racial and ethnic minorities, women, children and immigrants. Whilst the connection between socio-economic inequality and punishment inequality varies across nations, owing to differences in structural and cultural dimensions of power that shape penal practice, the specific effects of the inequality of punishment are evidenced in rural populations. These outcomes are related to international trends in the overuse of incarceration, the proliferation of drug control laws and policies and increased detention of immigrants (see Karstadt, 2021). Globally, over 11 million people are incarcerated, with prison overcrowding rates of 110 per cent across 102 nations. Echoing the United States’ mass incarceration ‘binge’, international rates of imprisonment grew in the first 15 years of the 2010s: Oceana – 59 per cent; Asia – 29 per cent; and Africa – 15 per cent. In England, Wales, the United States and Brazil, the percentage of Black and multi-race people in carceral facilities far exceeds their proportion in each nation’s general population. Similarly, Indigenous peoples are significantly over-represented in prisons in Canada and Australia. Whilst mass incarceration has been examined primarily through an ‘urban lens’, recent prison studies have identified a number of United States rural punishment inequality issues stemming from greater jail utilization, new prison and jail construction and opioids use. For example, the Vera Institute has reported that, compared with United States urban jails, rural county jails have higher rates of pretrial detention because of the lack of pretrial services and diversion programmes and the housing of inmates from overcrowded state and federal facilities.
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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 | 0.001 | 0.003 |
| 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 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.001 |
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".