Connecting business with the agricultural landscape: business strategies for sustainable rural development
Bibliographic record
Abstract
Abstract Agribusiness enterprises link rural landscapes to global and regional markets. The nature of these business–landscape relationships is vital to the sustainability transition. Decisions by farmers and agriculture policymakers aggregate to changes in the ecology of landscapes, but the influence of food supply system businesses on rural landscape sustainability also requires scrutiny. This article uses four international cases to present a conceptual framework for investigating how different business strategies can support agricultural landscape sustainability. Insights from North America, New Zealand, The Netherlands, and Denmark inform the framework dimensions of horizontal/territorial and vertical/systemic business–landscape relationships. Three types of business model that promote rural sustainability are highlighted: provenance, cogovernance, and placemaking. These models engage strategies such as environmental management systems, certification, ecosystem and landscape services, and spatial planning. Research directions that will improve understanding about how business can engage with rural stakeholders for more sustainable rural landscapes are identified, including the need for cross disciplinary perspectives incorporating social, ecological, and business knowledge.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".