Defining Rural: Advancing a Place-Based Rural Criteria Matrix
Bibliographic record
Abstract
What is rural? This question can be answered in many ways. Through quantitative measures such as population, density or remoteness thresholds and through concepts of socio-cultural understandings regarding lifestyle and identity. Given the vast diversity amongst rural areas and the concept of rural, it begs the question of whether a standard definition is even appropriate. However, the literature argues that for policy makers to address the clear distinction between rural and urban areas, to effectively serve rural communities and to define program eligibility, a definition is required. Comparing existing definitions and best practices highlights a need to push the conversation on a rural definition further and to create a place-based definition that captures the diversity of rural communities and incorporates multiple measures. Using British Columbia as a case study, a Rural Criteria Matrix is proposed as a new method for defining rural communities. The Matrix aims to meet the needs of policy makers and rural constituents, while moving beyond the binary, advancing reconciliation and recognizing the great heterogeneity amongst what is defined as “rural.”
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 | 0.020 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".