The integration of action research and traditional field research to provide sustainable solutions to maintaining periurban agriculture
Bibliographic record
Abstract
Abstract Maintaining periurban agriculture and prime periurban farmland has become a leitmotiv in land use planning and management around many cities in North America and Western Europe since the 1960s. This article focuses on the changing perspectives associated with these planning and management initiatives as well as changing research approaches. Initially, periurban farmland was often seen by planners as a land reserve for urban development. Subsequently, concern was expressed about maintaining the prime agricultural land resource via farmland protection programmes, especially in North America in the 1960s and 1970s. Early research into periurban agriculture involved statistical analyses of farmland losses and changing agricultural production systems, and farmer interviews to identify pressures and opportunities facing periurban agriculture. Gradually, it became clear that maintaining farmland resources and farm activities required more than just ‘protecting’ them from urbanisation. Two types of initiatives developed: (1) the construction of agricultural development plans to ensure sustainable farm development, e.g. in Quebec since 2008, in France since the mid‐1970s and more recently in Wallonia (Belgium) in 2014; and (2) a change in the research approach to support periurban agricultural sustainability. While still using interviews with farmers and other actors, more important is the emergence of action research to provide support to farmers, their neighbours, elected officials and professionals in developing agricultural development plans, with the aim of achieving a better integration of periurban agriculture into the regional urban system. This paper develops this reasoning using research in Canada, France, and principally Belgium to illustrate the argument.
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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 | 0.119 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.007 | 0.043 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".