FARMING FAMILIES AS FOSTER FAMILIES: THE FINDINGS OF AN EXPLORATORY STUDY ON CARE FARMING IN SWITZERLAND
Bibliographic record
Abstract
The terms “care farming” and “social agriculture” are used to describe the foster care that farming families provide to children, adolescents, and adults. Whereas some European countries have national systems that provide support for care farming, little is known about care farmers in Switzerland. Best estimates show that at least one percent of all agricultural family operations provide care services in Switzerland; accordingly, care farming is a component of Swiss foster care. Against the background of the recent revision of the Child and Adult Protection Act [Kindes- und Erwachsenenschutzgesetz] and of legal provisions in relation to foster care, a qualitative system analysis was carried out in three cantons in 2013. The aim of the system analysis was to describe the context and importance of care farming and to identify the attitudes and working methods of both child and adult protection authorities and family placement organizations in relation to placements in agriculture. As part of the study, documents were analyzed and expert interviews were held with representatives of both groups. The interviewed representatives of the placement authorities regard placements in agriculture as a viable option, in particular for adolescents, if the match between the client and foster family is suitable. According to the surveyed family placement organizations, the interest among farming families in offering foster places is considerable. The study presents care farming as one care service within a complex support system for children and adolescents, and raises new questions for investigation by more detailed research projects.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".