Bibliographic record
Abstract
To account for differences among rural and urban regions, the OECD s established a regional typology, classifying TL3 regions as predominantly urban (PU), intermediate (IN) or predominantly rural (PR) (OECD, 2009). This typology, based essentially on the percentage of regional population living in urban or rural communities, has proved to be meaningful to better explain regional differences in economic and labour market performance. However this typology does not take into account the presence of economic agglomerations if they happen to be in neighbouring regions. For example, a region is classified as rural or intermediate regardless its distance from a large urban centre where labour market, access to services, education opportunities and logistics for firms can be wider. Previous work reveals great heterogeneity in economic growth among rural regions and the distance from a populated centre could be a significant factor explaining these differences. For the latter, the OECD regional typology is extended to include an accessibility criterion. This criterion is based on the driving time needed for at least half of the population in a region to reach a populated centre of with 50 000 or more inhabitants. The resulting classification consists of four types of regions: Predominantly Urban (PU), Intermediate (IN), Predominantly Rural Close to a city (PRC) and Predominantly Rural Remote (PRR). For the time being, the extended typology has only been computed for regions in North America (Canada, Mexico and the United States) and Europe. The extended typology is used to compare the dynamics of population and labour markets. Remote rural regions show a stronger decline in population and a faster ageing process than rural regions close to a city. The remoteness of rural regions is in fact a significant factor explaining regional outflows of working age population, confirming that this extended typology captures the economic distance from market and services. Remote rural regions appear economically more fragile: lower employment rates (Canada and Mexico) and economic output (Europe).
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.017 | 0.024 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 0.016 |
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".