The Persistence of Family Farming: A Review of Explanatory Socio-economic and Historical Factors
Bibliographic record
Abstract
The family farm is a corner-institute of West European agriculture. This article highlights the main characteristics of the family farm and reviews both the socio-economic and political-institutional arguments used for the persistence of this structure in West European farming. At micro level, the socio-economic rationale behind the family farm states that economies of scale tend to increase the optimal farm size, but that this tendency is partly offset by the importance of transaction costs for monitoring labour results. Moreover, the flexibility of family labour, the accumulated human capital within the farming family and the ability to withstand hard (financial) times are factors in favour of the family farm. At macro level, the availability of food for the population has been one of the major concerns of policy makers. Different protectionist measures have been developed in order to secure enough food over time. Although the kind of farming system is not specified in these measures, the farm lobby has influenced the legislations in order to safeguard the current family farms. In the last decades the governmental concern has broadened due to environmental concerns. To reach these goals, the family farm approach is useful as family farms are essential for the kinds of landscape and rural social life. Taking into account the history of the family farm, the paper proposes different strategies, related to labour and capital allocation that can strengthen the survival of the family farm in the next decades.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".