Farmers as initiators and farms as attractors for non-agricultural economic activities in peri-urban areas in Denmark
Bibliographic record
Abstract
Geografisk Tidsskrift, Danish Journal of Geography 107(2):13–27, 2007 Changes in rural economy have been conceptualized as a post-pro-ductivistic transition with growing importance of non-agricultural economic activities as an important component. It is here hypothesized that these activities are profound in peri-urban areas and the importance of non-agricultural activities—here defined as on-farm business structure diversification—has been investigated on farm properties in an agricultural area in the vicinity of Copenhagen, Denmark. It has been explored what types of farm household adopt on-farm business structure diversification, and whether these activities evolved while living on the farm (farmers as initiators) or if opportunities for diversification were an important rationale for buying the farm property (farms as attractors). The case study area has 169 registered farm properties, and statistical inquiry (of business register information), questionnaire survey and qualitative interviews have been performed. The results show that agriculture is absent as an economic activity on half of the 169 farm properties. On a third of the farm properties, the buildings or their close surroundings are used for on-farm business structure diversification. It is a heterogeneous group of farmers that adopt diversification strategies, but especially the situation “farm as attractors” concerns many newcomers: They are attracted by the opportunities that the farm offers for economic activities traditionally located in urban areas. However, the attractor perspective is usually combined with other reasons for farm purchase. The total number of jobs generated by on-farm business structure diversification exceeds the employment in traditional farming. It is concluded that the peri-urban area to a certain extent has been transformed into a functionally urbanized countryside though it is still dominated by agricultural land use.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".