Above All, Farming Means Family Farming: Context for Introducing the Articles in This Special Issue
Bibliographic record
Abstract
This Special Issue presents research and theoretical developments concerning farm-family transitions. Specifically, how qualitative methods sometimes combined with quantitative approaches can bring new understanding to farmfamily functioning and the transitions experienced over several generations or years. The research provides rich details about transition points such as marriages and succession, and changes in production techniques or commodity. This information supports theory building about the family response to ecological (e.g., physical, economic, political, social) opportunities and constrains. The Special Issue authors offer useful conceptualizations, research strategies, and theory building that can enhance knowledge about the interplay of farm and family, business and lifestyle. Information is presented about family farming in a dozen or more countries. In this article the authors present background information about family farming as a context for introducing the articles in the Special Issue. An attempt is made to explain why the family farm is still a significant organisational element in farming, even in the industrialised-capitalist west. An explanation is given of how different farming paradigms (yeoman, entrepreneur) and farm-family types (Traditional farmers on the break-even point; Modernizers out of necessity, Part-time farmers, Innovative entrepreneurs) can lead to diverse strategies for responding to issues of modernity and changing agricultural conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".