Introduction to Part V: Geographic Status of Rural Criminological Research
Bibliographic record
Abstract
Despite rural criminology’s origins dating back to the 1930s, the growth of rural criminology has not been even across the globe. The ‘big four’ academic bases of rural criminological study – the United States, the United Kingdom, Australia and to a slightly lesser extent Canada – have hitherto dominated the scholarship landscape in book chapter and journal article form. This is, in part, attributable to the lure of wealthier, better-resourced institutions which happen to be located in these parts of the world. That is, scholars will relocate across borders to where jobs and opportunities exist, and then develop to an extent localized research interests. There are, of course, barriers which exist which make scholarship challenging in certain geographic places too, not just in terms of resourcing but prevailing research priorities. As with many other disciplines, much of the rural criminological literature is provided in English alone. The higher education sector places much weight on citations and other so-called metrics, which serves to isolate scholars performing vital and cutting-edge locally specific research. Many voices on crime and criminology beyond the urban places of the ‘big four’, therefore, have largely been excluded from traditional criminology. This section seeks to identify the geographic status of rural criminology based on the seven continents. Here we have utilized the CIA Factbook maps as the delineation between continents. Contributors were asked to restrict observations to approximately 1500 words – no mean feat when considering the huge and diverse populations, land masses and socio-cultural differences which exist between and within continents. However, what is presented here is a snapshot of key developments and, it is hoped, will serve as a portent of further geographical specific rural criminological work in the years ahead.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.046 | 0.012 |
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".