Introduction
Bibliographic record
Abstract
‘Rural’, most crudely, is defined as ‘non-urban’, but this dichotomous delineation is grossly inadequate because it neglects the consideration of the nuances of geography, demography, attitudes, culture and issues of access both tangible and amorphous. These are vitally important considerations: there exists significant cultural and spatial separation between urban and rural because what is taken for granted in the city is not accessible or available outside of it. There exists, most certainly, definitional difficulties about rural that will never go away. Should we just consider physical and demographic measures, such as population size and density, accessibility and remoteness? Such imprecision is typified by the existing definitions even within the same jurisdictions by different organizations and agencies of the same governmental units. Adopting a ‘one size fits all’ approach is unwise, though, as a universal measure will not account for the non-homogenous nature of geographic location, both within and across jurisdictions. For instance, a coastal location in Australia dominated with former city dwellers cannot be easily compared to a rapidly populated boom town in Canada reliant on imported labour, to a primarily agricultural community in Ireland with multiple generations of the same families present, to the Yanomamo and Kayapo and other tribes in the rain forest regions of South America, nor to a remote settlement in the Siberian region of Russia or in the state of Alaska in the United States. Indeed, different places have different cultural origins – as scholars such as Hayden, Weisheit et al, Donnermeyer and DeKeseredy, Ceccato, Harkness (see suggested readings) and many other scholars already have observed.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.484 | 0.312 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".